Store_Sales_Time_Series_Forecasting(1).ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
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      "source": [
        "train = pd.read_csv(\"/content/train.csv\")\n",
        "test = pd.read_csv(\"/content/test.csv\")\n",
        "oil = pd.read_csv(\"/content/oil.csv\")\n",
        "stores = pd.read_csv(\"/content/stores.csv\")\n",
        "transactions = pd.read_csv(\"/content/transactions.csv\")\n",
        "holidays = pd.read_csv(\"/content/holidays_events.csv\")"
      ],
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      "execution_count": 343,
      "outputs": []
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      "source": [
        "df1 = pd.merge(train, oil, on='date', how='left')\n",
        "df2 = pd.merge(df1, transactions, on=['date', 'store_nbr'], how='left')\n",
        "df3 = pd.merge(df2, stores, on='store_nbr', how='left')\n",
        "\n",
        "df3.head()"
      ],
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      "execution_count": 347,
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        {
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          "data": {
            "text/plain": [
              "   id        date  store_nbr      family  sales  onpromotion  dcoilwtico  \\\n",
              "0   0  2013-01-01          1  AUTOMOTIVE    0.0          0.0         NaN   \n",
              "1   1  2013-01-01          1   BABY CARE    0.0          0.0         NaN   \n",
              "2   2  2013-01-01          1      BEAUTY    0.0          0.0         NaN   \n",
              "3   3  2013-01-01          1   BEVERAGES    0.0          0.0         NaN   \n",
              "4   4  2013-01-01          1       BOOKS    0.0          0.0         NaN   \n",
              "\n",
              "   transactions   city      state type  cluster  \n",
              "0           NaN  Quito  Pichincha    D       13  \n",
              "1           NaN  Quito  Pichincha    D       13  \n",
              "2           NaN  Quito  Pichincha    D       13  \n",
              "3           NaN  Quito  Pichincha    D       13  \n",
              "4           NaN  Quito  Pichincha    D       13  "
            ],
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              "summary": "{\n  \"name\": \"df3\",\n  \"rows\": 81886,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23638,\n        \"min\": 0,\n        \"max\": 81885,\n        \"num_unique_values\": 81886,\n        \"samples\": [\n          73575,\n          14831,\n          6194\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 46,\n        \"samples\": [\n          \"2013-02-09\",\n          \"2013-01-26\",\n          \"2013-01-27\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"store_nbr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 1,\n        \"max\": 54,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          27,\n          54,\n          53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"family\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 33,\n        \"samples\": [\n          \"SCHOOL AND OFFICE SUPPLIES\",\n          \"HOME AND KITCHEN I\",\n          \"PET SUPPLIES\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 648.7062042572376,\n        \"min\": 0.0,\n        \"max\": 26067.0,\n        \"num_unique_values\": 12706,\n        \"samples\": [\n          175.617,\n          488.184,\n          15.459\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"onpromotion\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dcoilwtico\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.6131869799271585,\n        \"min\": 92.97,\n        \"max\": 97.98,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          97.01\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"transactions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 907.5240802570883,\n        \"min\": 374.0,\n        \"max\": 5401.0,\n        \"num_unique_values\": 1397,\n        \"samples\": [\n          1209.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 22,\n        \"samples\": [\n          \"Quito\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"Pichincha\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cluster\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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        "df4 = pd.merge(df3, holidays, on='date', how='left')"
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        "outputId": "af99848f-b9c1-4cd5-8f62-3862377c25a4"
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      "execution_count": 350,
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            " 12  description   8910 non-null   object \n",
            "dtypes: float64(4), int64(3), object(6)\n",
            "memory usage: 8.1+ MB\n"
          ]
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      "source": [
        "df.shape"
      ],
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        "id": "nzmQVNVgZ2wt",
        "outputId": "2fb36935-5f81-4370-c5b2-54185901d17f"
      },
      "execution_count": 351,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(81886, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 351
        }
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        "df.info()\n",
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      },
      "execution_count": 352,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 81886 entries, 0 to 81885\n",
            "Data columns (total 13 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   id            81886 non-null  int64  \n",
            " 1   date          81886 non-null  object \n",
            " 2   store_nbr     81886 non-null  int64  \n",
            " 3   family        81886 non-null  object \n",
            " 4   sales         81886 non-null  float64\n",
            " 5   onpromotion   81885 non-null  float64\n",
            " 6   dcoilwtico    56938 non-null  float64\n",
            " 7   transactions  68257 non-null  float64\n",
            " 8   city          81886 non-null  object \n",
            " 9   state         81886 non-null  object \n",
            " 10  type          81886 non-null  object \n",
            " 11  cluster       81886 non-null  int64  \n",
            " 12  description   8910 non-null   object \n",
            "dtypes: float64(4), int64(3), object(6)\n",
            "memory usage: 8.1+ MB\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "             id store_nbr     sales onpromotion dcoilwtico transactions  \\\n",
              "count  81886.00  81886.00  81886.00    81885.00   56938.00     68257.00   \n",
              "mean   40942.50     27.52    190.05        0.00      95.41      1677.52   \n",
              "std    23638.60     15.58    648.71        0.00       1.61       907.52   \n",
              "min        0.00      1.00      0.00        0.00      92.97       374.00   \n",
              "25%    20471.25     14.00      0.00        0.00      93.60      1039.00   \n",
              "50%    40942.50     28.00      0.00        0.00      95.61      1390.00   \n",
              "75%    61413.75     41.00     83.00        0.00      97.01      2129.00   \n",
              "max    81885.00     54.00  26067.00        0.00      97.98      5401.00   \n",
              "\n",
              "        cluster  \n",
              "count  81886.00  \n",
              "mean       8.48  \n",
              "std        4.65  \n",
              "min        1.00  \n",
              "25%        4.00  \n",
              "50%        9.00  \n",
              "75%       13.00  \n",
              "max       17.00  "
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              "      <th>mean</th>\n",
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              "      <td>4.65</td>\n",
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              "      <th>min</th>\n",
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              "      <th>25%</th>\n",
              "      <td>20471.25</td>\n",
              "      <td>14.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>93.60</td>\n",
              "      <td>1039.00</td>\n",
              "      <td>4.00</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>40942.50</td>\n",
              "      <td>28.00</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>95.61</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61413.75</td>\n",
              "      <td>41.00</td>\n",
              "      <td>83.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>97.01</td>\n",
              "      <td>2129.00</td>\n",
              "      <td>13.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
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              "      <td>54.00</td>\n",
              "      <td>26067.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>97.98</td>\n",
              "      <td>5401.00</td>\n",
              "      <td>17.00</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"81886.00\",\n          \"40942.50\",\n          \"61413.75\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"store_nbr\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"27.52\",\n          \"28.00\",\n          \"81886.00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"81886.00\",\n          \"190.05\",\n          \"26067.00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"onpromotion\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"0.00\",\n          \"81885.00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dcoilwtico\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"95.41\",\n          \"95.61\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"transactions\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"1677.52\",\n          \"1390.00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cluster\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"8.48\",\n          \"9.00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 352
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "holidays = pd.read_csv(\"/content/holidays_events.csv\")\n",
        "holidays.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "5Ep1KcK4w6py",
        "outputId": "57ca8f53-86f3-4c41-f3a6-9d3a9351afef"
      },
      "execution_count": 355,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         date     type    locale locale_name                    description  \\\n",
              "0  2012-03-02  Holiday     Local       Manta             Fundacion de Manta   \n",
              "1  2012-04-01  Holiday  Regional    Cotopaxi  Provincializacion de Cotopaxi   \n",
              "2  2012-04-12  Holiday     Local      Cuenca            Fundacion de Cuenca   \n",
              "3  2012-04-14  Holiday     Local    Libertad      Cantonizacion de Libertad   \n",
              "4  2012-04-21  Holiday     Local    Riobamba      Cantonizacion de Riobamba   \n",
              "\n",
              "   transferred  \n",
              "0        False  \n",
              "1        False  \n",
              "2        False  \n",
              "3        False  \n",
              "4        False  "
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2012-04-01</td>\n",
              "      <td>Holiday</td>\n",
              "      <td>Regional</td>\n",
              "      <td>Cotopaxi</td>\n",
              "      <td>Provincializacion de Cotopaxi</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2012-04-12</td>\n",
              "      <td>Holiday</td>\n",
              "      <td>Local</td>\n",
              "      <td>Cuenca</td>\n",
              "      <td>Fundacion de Cuenca</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2012-04-14</td>\n",
              "      <td>Holiday</td>\n",
              "      <td>Local</td>\n",
              "      <td>Libertad</td>\n",
              "      <td>Cantonizacion de Libertad</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2012-04-21</td>\n",
              "      <td>Holiday</td>\n",
              "      <td>Local</td>\n",
              "      <td>Riobamba</td>\n",
              "      <td>Cantonizacion de Riobamba</td>\n",
              "      <td>False</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "holidays",
              "summary": "{\n  \"name\": \"holidays\",\n  \"rows\": 350,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 312,\n        \"samples\": [\n          \"2016-05-16\",\n          \"2012-07-23\",\n          \"2013-08-10\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Holiday\",\n          \"Transfer\",\n          \"Event\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"locale\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Local\",\n          \"Regional\",\n          \"National\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"locale_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          \"Latacunga\",\n          \"Ibarra\",\n          \"Manta\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"description\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 103,\n        \"samples\": [\n          \"Fundacion de Loja\",\n          \"Terremoto Manabi+2\",\n          \"Cyber Monday\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"transferred\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 355
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "holidays = holidays[holidays.transferred != True]\n",
        "holidays = holidays[holidays.locale == 'National']\n",
        "\n",
        "holidays = holidays.drop(columns=['type', 'locale', 'locale_name', 'transferred'])"
      ],
      "metadata": {
        "id": "zsSAQFM8xDqO"
      },
      "execution_count": 356,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df4 = pd.merge(df3, holidays, on='date', how='left')\n",
        "df4.rename(columns={'description' : 'holiday'}, inplace=True)\n",
        "\n",
        "df = df4\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "MzqqrOPdxliI",
        "outputId": "b3132a5c-4873-4574-9c39-95494c18e5f7"
      },
      "execution_count": 357,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id        date  store_nbr      family  sales  onpromotion  dcoilwtico  \\\n",
              "0   0  2013-01-01          1  AUTOMOTIVE    0.0          0.0         NaN   \n",
              "1   1  2013-01-01          1   BABY CARE    0.0          0.0         NaN   \n",
              "2   2  2013-01-01          1      BEAUTY    0.0          0.0         NaN   \n",
              "3   3  2013-01-01          1   BEVERAGES    0.0          0.0         NaN   \n",
              "4   4  2013-01-01          1       BOOKS    0.0          0.0         NaN   \n",
              "\n",
              "   transactions   city      state type  cluster             holiday  \n",
              "0           NaN  Quito  Pichincha    D       13  Primer dia del ano  \n",
              "1           NaN  Quito  Pichincha    D       13  Primer dia del ano  \n",
              "2           NaN  Quito  Pichincha    D       13  Primer dia del ano  \n",
              "3           NaN  Quito  Pichincha    D       13  Primer dia del ano  \n",
              "4           NaN  Quito  Pichincha    D       13  Primer dia del ano  "
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            "text/html": [
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5767cc98-153a-4c0d-90be-333d1bc20854');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-829700d3-2ebd-4767-9de7-da142b058a1f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-829700d3-2ebd-4767-9de7-da142b058a1f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-829700d3-2ebd-4767-9de7-da142b058a1f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 81886,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23638,\n        \"min\": 0,\n        \"max\": 81885,\n        \"num_unique_values\": 81886,\n        \"samples\": [\n          73575,\n          14831,\n          6194\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 46,\n        \"samples\": [\n          \"2013-02-09\",\n          \"2013-01-26\",\n          \"2013-01-27\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"store_nbr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 1,\n        \"max\": 54,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          27,\n          54,\n          53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"family\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 33,\n        \"samples\": [\n          \"SCHOOL AND OFFICE SUPPLIES\",\n          \"HOME AND KITCHEN I\",\n          \"PET SUPPLIES\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 648.7062042572376,\n        \"min\": 0.0,\n        \"max\": 26067.0,\n        \"num_unique_values\": 12706,\n        \"samples\": [\n          175.617,\n          488.184,\n          15.459\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"onpromotion\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dcoilwtico\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.6131869799271585,\n        \"min\": 92.97,\n        \"max\": 97.98,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          97.01\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"transactions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 907.5240802570883,\n        \"min\": 374.0,\n        \"max\": 5401.0,\n        \"num_unique_values\": 1397,\n        \"samples\": [\n          1209.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 22,\n        \"samples\": [\n          \"Quito\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"Pichincha\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cluster\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"holiday\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Recupero puente Navidad\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 357
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# review new df\n",
        "\n",
        "df.info()\n",
        "df.describe().apply(lambda s: s.apply('{0:.2f}'.format))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "id": "Dz8m5z-Oxyxa",
        "outputId": "a5016cde-ae14-4f8a-b825-098aea6cea4a"
      },
      "execution_count": 359,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 81886 entries, 0 to 81885\n",
            "Data columns (total 13 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   id            81886 non-null  int64  \n",
            " 1   date          81886 non-null  object \n",
            " 2   store_nbr     81886 non-null  int64  \n",
            " 3   family        81886 non-null  object \n",
            " 4   sales         81886 non-null  float64\n",
            " 5   onpromotion   81885 non-null  float64\n",
            " 6   dcoilwtico    56938 non-null  float64\n",
            " 7   transactions  68257 non-null  float64\n",
            " 8   city          81886 non-null  object \n",
            " 9   state         81886 non-null  object \n",
            " 10  type          81886 non-null  object \n",
            " 11  cluster       81886 non-null  int64  \n",
            " 12  holiday       8910 non-null   object \n",
            "dtypes: float64(4), int64(3), object(6)\n",
            "memory usage: 8.1+ MB\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             id store_nbr     sales onpromotion dcoilwtico transactions  \\\n",
              "count  81886.00  81886.00  81886.00    81885.00   56938.00     68257.00   \n",
              "mean   40942.50     27.52    190.05        0.00      95.41      1677.52   \n",
              "std    23638.60     15.58    648.71        0.00       1.61       907.52   \n",
              "min        0.00      1.00      0.00        0.00      92.97       374.00   \n",
              "25%    20471.25     14.00      0.00        0.00      93.60      1039.00   \n",
              "50%    40942.50     28.00      0.00        0.00      95.61      1390.00   \n",
              "75%    61413.75     41.00     83.00        0.00      97.01      2129.00   \n",
              "max    81885.00     54.00  26067.00        0.00      97.98      5401.00   \n",
              "\n",
              "        cluster  \n",
              "count  81886.00  \n",
              "mean       8.48  \n",
              "std        4.65  \n",
              "min        1.00  \n",
              "25%        4.00  \n",
              "50%        9.00  \n",
              "75%       13.00  \n",
              "max       17.00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-91087474-6f86-4d9e-aee9-35f962b373ff\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>store_nbr</th>\n",
              "      <th>sales</th>\n",
              "      <th>onpromotion</th>\n",
              "      <th>dcoilwtico</th>\n",
              "      <th>transactions</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>81886.00</td>\n",
              "      <td>81886.00</td>\n",
              "      <td>81886.00</td>\n",
              "      <td>81885.00</td>\n",
              "      <td>56938.00</td>\n",
              "      <td>68257.00</td>\n",
              "      <td>81886.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>40942.50</td>\n",
              "      <td>27.52</td>\n",
              "      <td>190.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>95.41</td>\n",
              "      <td>1677.52</td>\n",
              "      <td>8.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>23638.60</td>\n",
              "      <td>15.58</td>\n",
              "      <td>648.71</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.61</td>\n",
              "      <td>907.52</td>\n",
              "      <td>4.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>92.97</td>\n",
              "      <td>374.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>20471.25</td>\n",
              "      <td>14.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>93.60</td>\n",
              "      <td>1039.00</td>\n",
              "      <td>4.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>40942.50</td>\n",
              "      <td>28.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>95.61</td>\n",
              "      <td>1390.00</td>\n",
              "      <td>9.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61413.75</td>\n",
              "      <td>41.00</td>\n",
              "      <td>83.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>97.01</td>\n",
              "      <td>2129.00</td>\n",
              "      <td>13.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>81885.00</td>\n",
              "      <td>54.00</td>\n",
              "      <td>26067.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>97.98</td>\n",
              "      <td>5401.00</td>\n",
              "      <td>17.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-91087474-6f86-4d9e-aee9-35f962b373ff')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-91087474-6f86-4d9e-aee9-35f962b373ff button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-91087474-6f86-4d9e-aee9-35f962b373ff');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
              "<div id=\"df-6411df7e-148f-43f9-a413-4baef320499f\">\n",
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              "            title=\"Suggest charts\"\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "      spin 1s steps(1) infinite;\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "        document.querySelector('#df-6411df7e-148f-43f9-a413-4baef320499f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"81886.00\",\n          \"40942.50\",\n          \"61413.75\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"store_nbr\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"27.52\",\n          \"28.00\",\n          \"81886.00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"81886.00\",\n          \"190.05\",\n          \"26067.00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"onpromotion\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"0.00\",\n          \"81885.00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dcoilwtico\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"95.41\",\n          \"95.61\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"transactions\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"1677.52\",\n          \"1390.00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cluster\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"8.48\",\n          \"9.00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 359
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zE5RnK0rashE",
        "outputId": "217baf28-5278-4778-8474-261c49fadd37"
      },
      "execution_count": 360,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                  0\n",
              "date                0\n",
              "store_nbr           0\n",
              "family              0\n",
              "sales               0\n",
              "onpromotion         1\n",
              "dcoilwtico      24948\n",
              "transactions    13629\n",
              "city                0\n",
              "state               0\n",
              "type                0\n",
              "cluster             0\n",
              "holiday         72976\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 360
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def nulls(x):\n",
        "  if x.isna().sum() >=1:\n",
        "    return x.interpolate().ffill().bfill()\n",
        "  else:\n",
        "    return x.ffill().bfill()\n",
        ""
      ],
      "metadata": {
        "id": "ek4bZyB8a4d8"
      },
      "execution_count": 361,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"dcoilwtico\"] = nulls(df[\"dcoilwtico\"])"
      ],
      "metadata": {
        "id": "G1pOblwbbfTD"
      },
      "execution_count": 362,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XxUbjVcwbzt7",
        "outputId": "28408ffe-669d-4289-8088-007dfe1a4fc9"
      },
      "execution_count": 363,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                  0\n",
              "date                0\n",
              "store_nbr           0\n",
              "family              0\n",
              "sales               0\n",
              "onpromotion         1\n",
              "dcoilwtico          0\n",
              "transactions    13629\n",
              "city                0\n",
              "state               0\n",
              "type                0\n",
              "cluster             0\n",
              "holiday         72976\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 363
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def date(x):\n",
        "    x['date'] = pd.to_datetime(x['date'])\n",
        "\n",
        "    x['year'] = x['date'].dt.year\n",
        "    x['month'] = x['date'].dt.month\n",
        "    x['day'] = x['date'].dt.day\n",
        "    x['day_name'] = x['date'].dt.day_name()\n",
        "    return x\n",
        "\n",
        "df = date(df)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "zw2rVTxFcGSJ",
        "outputId": "6253deda-feff-4cdd-fc0b-91f59b60d582"
      },
      "execution_count": 364,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id       date  store_nbr      family  sales  onpromotion  dcoilwtico  \\\n",
              "0   0 2013-01-01          1  AUTOMOTIVE    0.0          0.0       93.14   \n",
              "1   1 2013-01-01          1   BABY CARE    0.0          0.0       93.14   \n",
              "2   2 2013-01-01          1      BEAUTY    0.0          0.0       93.14   \n",
              "3   3 2013-01-01          1   BEVERAGES    0.0          0.0       93.14   \n",
              "4   4 2013-01-01          1       BOOKS    0.0          0.0       93.14   \n",
              "\n",
              "   transactions   city      state type  cluster             holiday  year  \\\n",
              "0           NaN  Quito  Pichincha    D       13  Primer dia del ano  2013   \n",
              "1           NaN  Quito  Pichincha    D       13  Primer dia del ano  2013   \n",
              "2           NaN  Quito  Pichincha    D       13  Primer dia del ano  2013   \n",
              "3           NaN  Quito  Pichincha    D       13  Primer dia del ano  2013   \n",
              "4           NaN  Quito  Pichincha    D       13  Primer dia del ano  2013   \n",
              "\n",
              "   month  day day_name  \n",
              "0      1    1  Tuesday  \n",
              "1      1    1  Tuesday  \n",
              "2      1    1  Tuesday  \n",
              "3      1    1  Tuesday  \n",
              "4      1    1  Tuesday  "
            ],
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              "  <div id=\"df-f22c2991-f8e5-4375-905a-0c9216aab334\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
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              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4881652c-dbab-43da-8551-8c0dbe064759 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 81886,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23638,\n        \"min\": 0,\n        \"max\": 81885,\n        \"num_unique_values\": 81886,\n        \"samples\": [\n          73575,\n          14831,\n          6194\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2013-01-01 00:00:00\",\n        \"max\": \"2013-02-15 00:00:00\",\n        \"num_unique_values\": 46,\n        \"samples\": [\n          \"2013-02-09 00:00:00\",\n          \"2013-01-26 00:00:00\",\n          \"2013-01-27 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"store_nbr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 1,\n        \"max\": 54,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          27,\n          54,\n          53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"family\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 33,\n        \"samples\": [\n          \"SCHOOL AND OFFICE SUPPLIES\",\n          \"HOME AND KITCHEN I\",\n          \"PET SUPPLIES\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 648.7062042572376,\n        \"min\": 0.0,\n        \"max\": 26067.0,\n        \"num_unique_values\": 12706,\n        \"samples\": [\n          175.617,\n          488.184,\n          15.459\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"onpromotion\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dcoilwtico\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5456413566078016,\n        \"min\": 92.97,\n        \"max\": 97.98,\n        \"num_unique_values\": 23122,\n        \"samples\": [\n          96.22744740532958\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"transactions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 907.5240802570883,\n        \"min\": 374.0,\n        \"max\": 5401.0,\n        \"num_unique_values\": 1397,\n        \"samples\": [\n          1209.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 22,\n        \"samples\": [\n          \"Quito\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"Pichincha\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cluster\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"holiday\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Recupero puente Navidad\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2013\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          28\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Tuesday\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 364
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = df.sample(n = 3000).reset_index()"
      ],
      "metadata": {
        "id": "LkaJpZwocPBe"
      },
      "execution_count": 365,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(df1 ,x =\"date\" , y = \"sales\" , c = \"red\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "RyWFUaX2comF",
        "outputId": "38a61a57-4957-435a-fb77-f7cffa9e8a97"
      },
      "execution_count": 366,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='date', ylabel='sales'>"
            ]
          },
          "metadata": {},
          "execution_count": 366
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axs = plt.subplots(1, 2, figsize=(20,7))\n",
        "\n",
        "sns.scatterplot(data=df1, x='onpromotion', y='sales', ax=axs[0])\n",
        "ax = sns.scatterplot(data=df1, x='dcoilwtico', y='sales', ax=axs[1])\n",
        "\n",
        "axs[0].set_title('Promotions vs Sales')\n",
        "axs[1].set_title('Oil Price vs Sales')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "fNaAf7-Zc9Oj",
        "outputId": "37db5225-3860-4395-85e8-25b7293804cb"
      },
      "execution_count": 367,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "fig, axs = plt.subplots(1, 2, figsize=(20,10))\n",
        "\n",
        "sns.barplot(x='sales', y='city', data=df1.groupby('city')['sales'].sum().reset_index().sort_values('sales', ascending = False), ax=axs[0])\n",
        "sns.barplot(x='sales', y='state', data=df1.groupby('state')['sales'].sum().reset_index().sort_values('sales', ascending = False), ax=axs[1])\n",
        "\n",
        "axs[0].set_title('Sales by City')\n",
        "axs[1].set_title('Sales by State')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 655
        },
        "id": "6HyepAIUdLsc",
        "outputId": "cc14c93d-9007-4f8e-9909-dc8bd38c94f9"
      },
      "execution_count": 368,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "familypivot = pd.DataFrame()\n",
        "familypivot = df.groupby(by='family')['sales'].sum().sort_values(ascending=False).reset_index()\n",
        "familypivot.head()\n",
        "\n",
        "# then evaluate by percentage of sales\n",
        "\n",
        "total = familypivot['sales'].sum()\n",
        "familypivot['percentage']=(familypivot['sales']/total)\n",
        "\n",
        "familypivot.head()\n",
        "fig, axs = plt.subplots(1, 2, figsize=(20,10))\n",
        "\n",
        "sns.barplot(data=familypivot, x='sales', y='family', ax=axs[0] , color = \"red\")\n",
        "sns.barplot(data=familypivot, x='percentage', y='family', ax=axs[1])\n",
        "\n",
        "axs[0].set_title('Sales by Product Type (Dollar Amount)')\n",
        "axs[1].set_title('Sales by Product Type (Percentage of Total Sales)')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "ax = sns.scatterplot(data=familypivot, x='sales', y='percentage' , color = \"orange\")\n",
        "ax.set_title('Total Sales vs Percentage of Total Sales')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "AwXMiZA2ficP",
        "outputId": "93f65860-fd07-45ba-f4a1-57d50e0c8490"
      },
      "execution_count": 369,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 2 Axes>"
            ],
            "image/png": 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uxuDBg7Ncx+rVqw1JRlxcnHHixAnjv//+M3744QcjODjYMJlMxqZNmwzDMIzBgwcbkozIyEir5RMSEgxJRo8ePaza+/XrZ0gyVq1aZYnH1dXVaNq0qZGWlmbp98477xiSjM6dO1va0td1s/j4eEOScfDgQcMwDOPs2bOGl5eXUa1aNePSpUtWfW9cR9OmTY2goKAs90NGTpw4YUiy2odffvmlIcnYtWuXpe3KlSuGv79/htvQvHlzqzF79uxpSDK2bdtmGIZhHDp0yHB2djY++OADq347duwwXFxcbNpvNnDgQEOSce7cOZt5koxevXplumzv3r2tYhkzZowhyZg2bZrVttWoUcPw9PQ0kpOTrca+cb/cfGwMI+O/rZdfftnImzevcfnyZUtbnTp1DEnGxIkTs9zWG82dO9eQZOzbt88wDMNITk423NzcjNGjR1v1Sz+/y5cvb/X3HxkZaZhMJqNx48ZW/WvUqGF1rmT3/DYM232SLigoyOrcSN9XTz/9tNV5+sYbbxjOzs5Wf8flypWz+t652YgRIwxJxrFjxzLtAwAAgAcL+Sz5LPns9fOne/fuxokTJ4zjx48bv/32m9GgQQNDkvHJJ58YhmEYDRo0MMLCwqzyy7S0NKNmzZpGqVKlLG3p+/7JJ580UlNTLe3ZOQZpaWlGqVKljPDwcKvjcvHiRaN48eJGw4YNbfZbt27drMZq2bKl4efnZ9Xm4eFhtc9vHPdmGzZsMCQZU6dOtbRFRUUZJpPJ2Lp1q6Xt1KlTRv78+a3Os3Pnzhm+vr7Giy++aDXm0aNHDR8fH5v2m/Xp08eQZPVdce7cOaN48eJGcHCwce3aNUv7rX6DyMimTZsMSUZ8fLxhGNfPx4IFCxrly5e3OiaLFi0yJBmDBg2ytPXq1SvDvzXDsN2PV65cMcqXL2/Ur1/fqv3mfD2zscqUKWNIMoKCgowuXboYsbGxGebhGR2/mTNnGpKMdevWWdpu/j7I7nE6c+aMIckYNWpUljGnc3V1NV599dVs9QVyEx6lDgBADjVs2FAbNmxQ8+bNtW3bNo0cOVLh4eEqUqSIzSO13N3dLf+flJSkkydPqk6dOjpw4ICSkpIyXcecOXP06KOPKjQ0VCdPnrRM6Y9sW716tSRZro5esGCB0tLScrwtNWrUsFzlKV2/07dFixZatmyZ5ZHUnTp1UkpKitVjoWbNmqXU1NRsv6OpW7duKlCggAoXLqymTZtaHutVpUoVq36vvPKK1efFixdLkt58802r9vQ7edMfr7VixQpduXJFUVFRVlfI9unTJ1vxZWT58uU6d+6c3n77bZv3kd2r9ye1bdtWbm5uVo/tXrZsmU6ePJnhvr75Lo2oqChJ/7ffvv32W6Wlpalt27ZW51FAQIBKlSplOY8yc+rUKbm4uNzWXQPpy5w7d84SU0BAgCIjIy198uTJo9dff13nz5/X2rVrczT+jX9b586d08mTJ/XUU0/p4sWL2r17t1Vfs9msrl27Znvs6dOnq0qVKpY7BdIfMZbR49Sl638jefLksXyuVq2aDMOweTd3tWrVdOTIEaWmpkrK/vl9O1566SWr8/Spp57StWvXdPjw4WyPkS9fPknX3x8IAAAAx0A+Sz5LPntdbGysChQooIIFC6patWqW11H16dNHp0+f1qpVq9S2bVtLvnny5EmdOnVK4eHh2rdvn9WjtyXpxRdflLOzs+Vzdo5BQkKC9u3bpw4dOujUqVOW9Vy4cEENGjTQunXrbP42bj7PnnrqKZ06dUrJyclZ7g/J+m/66tWrOnXqlEJCQuTr66vff//dMm/p0qWqUaOGKlWqZGnLnz+/nn/+eavxli9frrNnzyoyMtLqGDk7O6tatWq3PEaLFy/WE088YXn1gHT9t4SXXnpJhw4d0p9//nnLbcqJzZs36/jx4+rZs6fVMWnatKlCQ0OznYPfuB/PnDmjpKQkPfXUU1b7MLvc3d3122+/6a233pJ0/Y727t27q1ChQoqKirJ67cKN6718+bJOnjyp6tWrS1KW687ucXJ3d5erq6vWrFmjM2fO3DL2fPny8XsBHkg8Sh0AgNtQtWpVffvtt7py5Yq2bdum7777TqNHj1br1q2VkJCgsmXLSrr+SKHBgwdrw4YNVu98kq7/sODj45Ph+Pv27dOuXbtUoECBDOcfP35cktSuXTtNmjRJPXr00Ntvv60GDRroueeeU+vWreXkdOvr30qVKmXTVrp0aV28eFEnTpxQQECAQkNDVbVqVU2fPl3du3eXdL1wWL16dUvh8FYGDRqkp556Ss7OzvL399ejjz4qFxfbf4YUL17c6vPhw4fl5ORks56AgAD5+vpainzp/715ewoUKGAp7uVU+uPxbvV4wbvJ19dXzzzzjGbMmKH3339f0vV9XaRIEcuPSDe6eXtLliwpJycny7uf9u3bJ8MwMjzOkqyKuXfb+fPnJV0vKkvXj1GpUqVszstHH33UMj8ndu7cqYEDB2rVqlU2PwDc/CNdkSJF5Orqmq1xz549q8WLF+u1117T/v37Le21atXSvHnztHfvXpUuXdpqmWLFill9Tv+7DgwMtGlPS0tTUlKS/Pz8sn1+346bY0r/O8hOcpvO+P+Pf7xXP5wBAADAPshnyWfvhQctn23RooVee+01mUwmeXl5qVy5cvLw8JAk7d+/X4Zh6L333tN7772X4fLHjx9XkSJFLJ9vPv7ZOQb79u2TJHXu3DnTPklJSVbnQVa53o3vo87IpUuXFBMTo/j4eP3777+WnC99PekOHz6c4WsGbj6X0+PP6PhKumU8hw8fVrVq1Wzab/yd4G6ew+l/a2XKlLGZFxoaqp9//jlb4yxatEjDhw9XQkKCVeH6dnNnHx8fjRw5UiNHjtThw4e1cuVKffzxxxo7dqx8fHw0fPhwSdLp06c1dOhQffPNN5bv0XRZXayU3eNkNpv10UcfqW/fvnrkkUdUvXp1NWvWTJ06dcrwtXKGYfB7AR5IFMYBALgDrq6uqlq1qqpWrarSpUura9eumjNnjgYPHqy//vpLDRo0UGhoqD799FMFBgbK1dVVixcv1ujRo7O8Ij4tLU1hYWH69NNPM5yfXnBzd3fXunXrtHr1av3www9aunSpZs2apfr16+vHH3+0ulr5TnTq1Em9e/fWP//8o5SUFP36668aO3ZstpcPCwvT008/fct+N179eqO7+Q/tzMZKv6PA3jp16qQ5c+Zo/fr1CgsL08KFC9WzZ89s/TB087alpaXJZDJpyZIlGZ4Lt7oT3M/PT6mpqTp37pylwJ1df/zxh5ydnW1+HLgbzp49qzp16sjb21vDhg1TyZIl5ebmpt9//10DBgyw+dvK7LzKyJw5c5SSkqJPPvlEn3zyic386dOna+jQoVZtmf2dZdZ+448P0p2d35mdt9ldd1bSi+j+/v45DwwAAAC5Hvls9pDPZt+DlM8WLVo00+Oafn7369dP4eHhGfa5uUick7zz5vWMGjXK6u7sG928nXeS60VFRSk+Pl59+vRRjRo15OPjI5PJpPbt29/WUxvSl/n6668zLJxmdAHJg+6nn35S8+bNVbt2bY0fP16FChVSnjx5FB8frxkzZtzx+EFBQerWrZtatmypEiVKaPr06ZbCeNu2bbV+/Xq99dZbqlSpkjw9PZWWlqaIiIhbfidL2TtOffr00TPPPKP58+dr2bJleu+99xQTE6NVq1bZvEv87Nmz/F6AB5LjfTMBAGAn6Y9RS0xMlCR9//33SklJ0cKFC62u6L3Vo6Sk61dKb9u2TQ0aNLhlEu3k5KQGDRqoQYMG+vTTTzVixAi9++67Wr169S2T9/SrRm+0d+9e5c2b1+rq/vbt2+vNN9/UzJkzdenSJeXJk0ft2rW75XbcqaCgIKWlpWnfvn2WK4Yl6dixYzp79qyCgoIs/aTr21OiRAlLvxMnTtjcIZt+NfXZs2ctj+6TbO9YLlmypKTrBd6s7iS421fHRkREqECBApo+fbqqVaumixcv6oUXXsiw7759+6wKz/v371daWpqCg4MlXd8GwzBUvHhxm7ucsyM0NFSSdPDgQVWoUCHby/39999au3atatSoYfkBIigoSNu3b1daWprVjyLpjz1PP4bZsWbNGp06dUrffvutateubWk/ePBgtsfIzPTp01W+fHkNHjzYZt6XX36pGTNm2BTGb1d2z2/p+nl79uxZq+WvXLli+b65Hbc6dw8ePCh/f/9M7/QBAACA4yCfvfvIZx/MfFaS5TjkyZMnWxdFZCQ7xyC9j7e3922vJyOZHde5c+eqc+fOVheBX7582SbXDAoKsnqCWrqb29LjL1iw4G3FHxQUpD179ti0387vBNldnyTt2bPH5u7pPXv2WK0vs304b948ubm5admyZTKbzZb2+Pj4uxprvnz5VLJkSf3xxx+Srl+4vnLlSg0dOlSDBg2y9Mvoe/BmOT1OJUuWVN++fdW3b1/t27dPlSpV0ieffKJp06ZZ+vz777+6cuWK1Xcb8KDgHeMAAOTQ6tWrM7wSN/09WOmPZEq/ivfmR1Nl5x/Lbdu21b///qv//e9/NvMuXbqkCxcuSLr+GKWbpV9lfOPjnDKzYcMGq/cQHTlyRAsWLFCjRo2srkL29/dX48aNNW3aNE2fPl0RERH35arQJk2aSJLGjBlj1Z5+50HTpk0lSU8//bTy5MmjL774wmp/37yc9H8Jwbp16yxt6e+Iu1GjRo3k5eWlmJgYXb582Wrejevw8PDI8pFVOeXi4qLIyEjNnj1bkydPVlhYWKZJ/Lhx46w+f/HFF5Kkxo0bS5Kee+45OTs7a+jQoTbnrGEYOnXqVJaxpD86bfPmzdmO//Tp04qMjNS1a9f07rvvWtqbNGmio0ePatasWZa21NRUffHFF/L09FSdOnWyvY6M/rauXLmi8ePHZ3uMjBw5ckTr1q1T27Zt1bp1a5upa9eu2r9/v3777bc7Wk+67J7f0vXz9sZzVpK++uqrO7ozxMP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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "family_map       = {'AUTOMOTIVE': 'rest',\n",
        "                   'BABY CARE': 'rest',\n",
        "                   'BEAUTY': 'rest',\n",
        "                   'BOOKS': 'rest',\n",
        "                   'CELEBRATION': 'rest',\n",
        "                   'GROCERY II': 'rest',\n",
        "                   'HARDWARE': 'rest',\n",
        "                   'HOME AND KITCHEN I': 'rest',\n",
        "                   'HOME AND KITCHEN II': 'rest',\n",
        "                   'HOME APPLIANCES': 'rest',\n",
        "                   'LADIESWEAR': 'rest',\n",
        "                   'LAWN AND GARDEN': 'rest',\n",
        "                   'LINGERIE': 'rest',\n",
        "                   'MAGAZINES': 'rest',\n",
        "                   'PET SUPPLIES': 'rest',\n",
        "                   'PLAYERS AND ELECTRONICS': 'rest',\n",
        "                   'SCHOOL AND OFFICE SUPPLIES': 'rest',\n",
        "                   'SEAFOOD': 'rest',\n",
        "                   'DELI': 'fifth',\n",
        "                    'EGGS': 'sixth',\n",
        "                    'FROZEN FOODS': 'sixth',\n",
        "                    'HOME CARE': 'sixth',\n",
        "                    'LIQUOR,WINE,BEER': 'sixth',\n",
        "                    'PREPARED FOODS': 'sixth',\n",
        "                    'PERSONAL CARE': 'fifth',\n",
        "                    'BREAD/BAKERY': 'fourth',\n",
        "                    'MEATS': 'fifth',\n",
        "                    'POULTRY': 'third',\n",
        "                    'CLEANING':'third',\n",
        "                    'DAIRY':'fourth',\n",
        "                    'PRODUCE':'third',\n",
        "                    'BEVERAGES':'second',\n",
        "                    'GROCERY I': 'first'\n",
        "                   }\n",
        "\n",
        "df['new_family'] = df['family'].map(family_map)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "8k6F5vaHoMUy",
        "outputId": "9d6f5924-14a8-4eb4-d893-1d205c0e4b81"
      },
      "execution_count": 370,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id       date  store_nbr      family  sales  onpromotion  dcoilwtico  \\\n",
              "0   0 2013-01-01          1  AUTOMOTIVE    0.0          0.0       93.14   \n",
              "1   1 2013-01-01          1   BABY CARE    0.0          0.0       93.14   \n",
              "2   2 2013-01-01          1      BEAUTY    0.0          0.0       93.14   \n",
              "3   3 2013-01-01          1   BEVERAGES    0.0          0.0       93.14   \n",
              "4   4 2013-01-01          1       BOOKS    0.0          0.0       93.14   \n",
              "\n",
              "   transactions   city      state type  cluster             holiday  year  \\\n",
              "0           NaN  Quito  Pichincha    D       13  Primer dia del ano  2013   \n",
              "1           NaN  Quito  Pichincha    D       13  Primer dia del ano  2013   \n",
              "2           NaN  Quito  Pichincha    D       13  Primer dia del ano  2013   \n",
              "3           NaN  Quito  Pichincha    D       13  Primer dia del ano  2013   \n",
              "4           NaN  Quito  Pichincha    D       13  Primer dia del ano  2013   \n",
              "\n",
              "   month  day day_name new_family  \n",
              "0      1    1  Tuesday       rest  \n",
              "1      1    1  Tuesday       rest  \n",
              "2      1    1  Tuesday       rest  \n",
              "3      1    1  Tuesday     second  \n",
              "4      1    1  Tuesday       rest  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e13ae14f-eaea-4db4-943f-d53a7d0e2a01\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>date</th>\n",
              "      <th>store_nbr</th>\n",
              "      <th>family</th>\n",
              "      <th>sales</th>\n",
              "      <th>onpromotion</th>\n",
              "      <th>dcoilwtico</th>\n",
              "      <th>transactions</th>\n",
              "      <th>city</th>\n",
              "      <th>state</th>\n",
              "      <th>type</th>\n",
              "      <th>cluster</th>\n",
              "      <th>holiday</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>day_name</th>\n",
              "      <th>new_family</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2013-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>AUTOMOTIVE</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>93.14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Quito</td>\n",
              "      <td>Pichincha</td>\n",
              "      <td>D</td>\n",
              "      <td>13</td>\n",
              "      <td>Primer dia del ano</td>\n",
              "      <td>2013</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>rest</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
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              "      <td>3</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2013-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>BOOKS</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>93.14</td>\n",
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              "      <td>Primer dia del ano</td>\n",
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              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "    (() => {\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 81886,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23638,\n        \"min\": 0,\n        \"max\": 81885,\n        \"num_unique_values\": 81886,\n        \"samples\": [\n          73575,\n          14831,\n          6194\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2013-01-01 00:00:00\",\n        \"max\": \"2013-02-15 00:00:00\",\n        \"num_unique_values\": 46,\n        \"samples\": [\n          \"2013-02-09 00:00:00\",\n          \"2013-01-26 00:00:00\",\n          \"2013-01-27 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"store_nbr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 1,\n        \"max\": 54,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          27,\n          54,\n          53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"family\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 33,\n        \"samples\": [\n          \"SCHOOL AND OFFICE SUPPLIES\",\n          \"HOME AND KITCHEN I\",\n          \"PET SUPPLIES\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 648.7062042572376,\n        \"min\": 0.0,\n        \"max\": 26067.0,\n        \"num_unique_values\": 12706,\n        \"samples\": [\n          175.617,\n          488.184,\n          15.459\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"onpromotion\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dcoilwtico\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5456413566078016,\n        \"min\": 92.97,\n        \"max\": 97.98,\n        \"num_unique_values\": 23122,\n        \"samples\": [\n          96.22744740532958\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"transactions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 907.5240802570883,\n        \"min\": 374.0,\n        \"max\": 5401.0,\n        \"num_unique_values\": 1397,\n        \"samples\": [\n          1209.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 22,\n        \"samples\": [\n          \"Quito\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"Pichincha\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cluster\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"holiday\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Recupero puente Navidad\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2013\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          28\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Tuesday\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"new_family\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"rest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 370
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df2 = df.drop(columns=['family'])\n",
        "df2.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "XGzOgfaEoRGb",
        "outputId": "2dedd486-31c8-4866-b03f-f9164ba3f807"
      },
      "execution_count": 371,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id       date  store_nbr  sales  onpromotion  dcoilwtico  transactions  \\\n",
              "0   0 2013-01-01          1    0.0          0.0       93.14           NaN   \n",
              "1   1 2013-01-01          1    0.0          0.0       93.14           NaN   \n",
              "2   2 2013-01-01          1    0.0          0.0       93.14           NaN   \n",
              "3   3 2013-01-01          1    0.0          0.0       93.14           NaN   \n",
              "4   4 2013-01-01          1    0.0          0.0       93.14           NaN   \n",
              "\n",
              "    city      state type  cluster             holiday  year  month  day  \\\n",
              "0  Quito  Pichincha    D       13  Primer dia del ano  2013      1    1   \n",
              "1  Quito  Pichincha    D       13  Primer dia del ano  2013      1    1   \n",
              "2  Quito  Pichincha    D       13  Primer dia del ano  2013      1    1   \n",
              "3  Quito  Pichincha    D       13  Primer dia del ano  2013      1    1   \n",
              "4  Quito  Pichincha    D       13  Primer dia del ano  2013      1    1   \n",
              "\n",
              "  day_name new_family  \n",
              "0  Tuesday       rest  \n",
              "1  Tuesday       rest  \n",
              "2  Tuesday       rest  \n",
              "3  Tuesday     second  \n",
              "4  Tuesday       rest  "
            ],
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              "      <td>1</td>\n",
              "      <td>2013-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>93.14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Quito</td>\n",
              "      <td>Pichincha</td>\n",
              "      <td>D</td>\n",
              "      <td>13</td>\n",
              "      <td>Primer dia del ano</td>\n",
              "      <td>2013</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>rest</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2013-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>93.14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Quito</td>\n",
              "      <td>Pichincha</td>\n",
              "      <td>D</td>\n",
              "      <td>13</td>\n",
              "      <td>Primer dia del ano</td>\n",
              "      <td>2013</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>rest</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2013-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>93.14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Quito</td>\n",
              "      <td>Pichincha</td>\n",
              "      <td>D</td>\n",
              "      <td>13</td>\n",
              "      <td>Primer dia del ano</td>\n",
              "      <td>2013</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>second</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2013-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>93.14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Quito</td>\n",
              "      <td>Pichincha</td>\n",
              "      <td>D</td>\n",
              "      <td>13</td>\n",
              "      <td>Primer dia del ano</td>\n",
              "      <td>2013</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>rest</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e2c1f408-1599-4baf-acee-20c811920d56')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e2c1f408-1599-4baf-acee-20c811920d56 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e2c1f408-1599-4baf-acee-20c811920d56');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5bff973e-19e7-4889-8297-4794b040f8e2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5bff973e-19e7-4889-8297-4794b040f8e2')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5bff973e-19e7-4889-8297-4794b040f8e2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df2",
              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 81886,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23638,\n        \"min\": 0,\n        \"max\": 81885,\n        \"num_unique_values\": 81886,\n        \"samples\": [\n          73575,\n          14831,\n          6194\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2013-01-01 00:00:00\",\n        \"max\": \"2013-02-15 00:00:00\",\n        \"num_unique_values\": 46,\n        \"samples\": [\n          \"2013-02-09 00:00:00\",\n          \"2013-01-26 00:00:00\",\n          \"2013-01-27 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"store_nbr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 1,\n        \"max\": 54,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          27,\n          54,\n          53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 648.7062042572376,\n        \"min\": 0.0,\n        \"max\": 26067.0,\n        \"num_unique_values\": 12706,\n        \"samples\": [\n          175.617,\n          488.184,\n          15.459\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"onpromotion\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dcoilwtico\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5456413566078016,\n        \"min\": 92.97,\n        \"max\": 97.98,\n        \"num_unique_values\": 23122,\n        \"samples\": [\n          96.22744740532958\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"transactions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 907.5240802570883,\n        \"min\": 374.0,\n        \"max\": 5401.0,\n        \"num_unique_values\": 1397,\n        \"samples\": [\n          1209.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 22,\n        \"samples\": [\n          \"Quito\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"Pichincha\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cluster\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"holiday\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Recupero puente Navidad\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2013\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          28\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Tuesday\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"new_family\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"rest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 371
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "7cVGSt43dg1a"
      },
      "execution_count": 372,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df2.drop(columns = \"sales\")\n",
        "Y = df2[\"sales\"]"
      ],
      "metadata": {
        "id": "4PHvBFyoeVx-"
      },
      "execution_count": 373,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "types = X.select_dtypes(exclude=np.number).columns.tolist()\n",
        "\n",
        "for col in types:\n",
        "    X[col] = X[col].astype('category')\n",
        "\n",
        "X.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bjSS13nTdvZB",
        "outputId": "4823f9e6-6d2b-4c8d-f626-c6e0f336213b"
      },
      "execution_count": 374,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                 int64\n",
              "date            category\n",
              "store_nbr          int64\n",
              "onpromotion      float64\n",
              "dcoilwtico       float64\n",
              "transactions     float64\n",
              "city            category\n",
              "state           category\n",
              "type            category\n",
              "cluster            int64\n",
              "holiday         category\n",
              "year               int32\n",
              "month              int32\n",
              "day                int32\n",
              "day_name        category\n",
              "new_family      category\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 374
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, random_state=2 , test_size = 0.2)\n",
        "\n",
        "import xgboost as xgb\n",
        "\n",
        "df_train = xgb.DMatrix(X_train, Y_train, enable_categorical=True)\n",
        "df_test = xgb.DMatrix(X_test, Y_test, enable_categorical=True)"
      ],
      "metadata": {
        "id": "7C_eVmHueGlD"
      },
      "execution_count": 375,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "params = {\"objective\": \"reg:squarederror\"}\n",
        "evals = [(df_train, \"train\"), (df_test, \"validation\")]\n",
        "n = 5000\n",
        "\n",
        "model = xgb.train(\n",
        "   params=params,\n",
        "   dtrain=df_train,\n",
        "   num_boost_round=n,\n",
        "   evals=evals,\n",
        "   verbose_eval=250, # every 250 rounds\n",
        "   early_stopping_rounds=50 # stop the training if validation loss doesn't improve for 50 consecutive rounds\n",
        ")\n",
        "\n",
        "preds = model.predict(df_test)\n",
        "\n",
        "\n",
        "rmse = mean_squared_error(Y_test, preds, squared=False)\n",
        "\n",
        "\n",
        "print(f\"RMSE: {rmse:.3f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qZ3UkoXWeOXt",
        "outputId": "205ec023-9b9a-42d1-8b82-ba5e9fe4d301"
      },
      "execution_count": 376,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\ttrain-rmse:491.92156\tvalidation-rmse:452.38719\n",
            "[62]\ttrain-rmse:155.64245\tvalidation-rmse:217.49983\n",
            "RMSE: 217.500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test = pd.read_csv(\"/content/test.csv\")"
      ],
      "metadata": {
        "id": "Yzs1GXDtotNS"
      },
      "execution_count": 377,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ZITsVWtco0ZS",
        "outputId": "45f57841-9a38-4ed3-f988-29da7ee0eae3"
      },
      "execution_count": 378,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        id        date  store_nbr      family  onpromotion\n",
              "0  3000888  2017-08-16          1  AUTOMOTIVE            0\n",
              "1  3000889  2017-08-16          1   BABY CARE            0\n",
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              "type": "dataframe",
              "variable_name": "test",
              "summary": "{\n  \"name\": \"test\",\n  \"rows\": 28512,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8230,\n        \"min\": 3000888,\n        \"max\": 3029399,\n        \"num_unique_values\": 28512,\n        \"samples\": [\n          3013543,\n          3017927,\n          3003169\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"2017-08-16\",\n          \"2017-08-17\",\n          \"2017-08-21\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"store_nbr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 1,\n        \"max\": 54,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          27,\n          54,\n          53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"family\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 33,\n        \"samples\": [\n          \"SCHOOL AND OFFICE SUPPLIES\",\n          \"HOME AND KITCHEN I\",\n          \"PET SUPPLIES\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"onpromotion\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20,\n        \"min\": 0,\n        \"max\": 646,\n        \"num_unique_values\": 212,\n        \"samples\": [\n          163,\n          224,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 378
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def replace_non_null(x):\n",
        "    x.loc[x['holiday'].notnull(), 'holiday'] = 'yes'\n",
        "    return x\n",
        "\n",
        "df2 = replace_non_null(df2)"
      ],
      "metadata": {
        "id": "kK-S6NNsyUm6"
      },
      "execution_count": 379,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merge1 = pd.merge(test, oil, on='date', how='left')\n",
        "merge2 = pd.merge(merge1, transactions, on=['date', 'store_nbr'], how='left')\n",
        "merge3 = pd.merge(merge2, stores, on='store_nbr', how='left')\n",
        "\n",
        "merge4 = pd.merge(merge3, holidays, on='date', how='left')\n",
        "merge4.rename(columns={'description' : 'holiday'}, inplace=True)\n",
        "\n",
        "test = merge4\n",
        "\n",
        "# adding oil prices and date features\n",
        "\n",
        "test['dcoilwtico'] = nulls(test['dcoilwtico'])\n",
        "test = date(test)\n",
        "\n",
        "\n",
        "# replace family\n",
        "\n",
        "test['new_family'] = test['family'].map(family_map)\n",
        "test = test.drop(columns=['family'])\n",
        "\n",
        "# replace holidays with yes or null\n",
        "\n",
        "test = replace_non_null(test)\n",
        "\n",
        "test.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "gMZXp8UgsDnT",
        "outputId": "674eae91-f9ff-4cf0-da8d-6422b1e897fe"
      },
      "execution_count": 380,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "        id       date  store_nbr  onpromotion  dcoilwtico  transactions  \\\n",
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              "4  3000892 2017-08-16          1            0        46.8           NaN   \n",
              "\n",
              "    city      state type  cluster holiday  year  month  day   day_name  \\\n",
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              "\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8d3297c0-d567-4a29-9333-49063828c7ff')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8d3297c0-d567-4a29-9333-49063828c7ff button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8d3297c0-d567-4a29-9333-49063828c7ff');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-2ef44358-1a2a-46b5-8bdc-01f660d21e61\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2ef44358-1a2a-46b5-8bdc-01f660d21e61')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2ef44358-1a2a-46b5-8bdc-01f660d21e61 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test",
              "repr_error": "Out of range float values are not JSON compliant: nan"
            }
          },
          "metadata": {},
          "execution_count": 380
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cats = test.select_dtypes(exclude=np.number).columns.tolist()\n",
        "\n",
        "for col in cats:\n",
        "    test[col] = test[col].astype('category')"
      ],
      "metadata": {
        "id": "Z4IJZcxlsu5t"
      },
      "execution_count": 382,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "rcKBiStIyqaa",
        "outputId": "1171392f-e417-4772-f833-05553f88d323"
      },
      "execution_count": 383,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        id       date  store_nbr  onpromotion  dcoilwtico  transactions  \\\n",
              "0  3000888 2017-08-16          1            0        46.8           NaN   \n",
              "1  3000889 2017-08-16          1            0        46.8           NaN   \n",
              "2  3000890 2017-08-16          1            2        46.8           NaN   \n",
              "3  3000891 2017-08-16          1           20        46.8           NaN   \n",
              "4  3000892 2017-08-16          1            0        46.8           NaN   \n",
              "\n",
              "    city      state type  cluster holiday  year  month  day   day_name  \\\n",
              "0  Quito  Pichincha    D       13     NaN  2017      8   16  Wednesday   \n",
              "1  Quito  Pichincha    D       13     NaN  2017      8   16  Wednesday   \n",
              "2  Quito  Pichincha    D       13     NaN  2017      8   16  Wednesday   \n",
              "3  Quito  Pichincha    D       13     NaN  2017      8   16  Wednesday   \n",
              "4  Quito  Pichincha    D       13     NaN  2017      8   16  Wednesday   \n",
              "\n",
              "  new_family  \n",
              "0       rest  \n",
              "1       rest  \n",
              "2       rest  \n",
              "3     second  \n",
              "4       rest  "
            ],
            "text/html": [
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              "  <div id=\"df-cce69e88-4fe1-4af8-94d1-fa2cbd381fb0\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th>type</th>\n",
              "      <th>cluster</th>\n",
              "      <th>holiday</th>\n",
              "      <th>year</th>\n",
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              "      <th>day</th>\n",
              "      <th>day_name</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>NaN</td>\n",
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              "      <td>Pichincha</td>\n",
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              "      <th>2</th>\n",
              "      <td>3000890</td>\n",
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              "      <td>46.8</td>\n",
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              "      <td>20</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>2017</td>\n",
              "      <td>8</td>\n",
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              "      <td>Wednesday</td>\n",
              "      <td>second</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3000892</td>\n",
              "      <td>2017-08-16</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cce69e88-4fe1-4af8-94d1-fa2cbd381fb0 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cce69e88-4fe1-4af8-94d1-fa2cbd381fb0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4c3877f6-b3b3-4304-86b1-ad55fef89545')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4c3877f6-b3b3-4304-86b1-ad55fef89545 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test",
              "summary": "{\n  \"name\": \"test\",\n  \"rows\": 28512,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8230,\n        \"min\": 3000888,\n        \"max\": 3029399,\n        \"num_unique_values\": 28512,\n        \"samples\": [\n          3013543,\n          3017927,\n          3003169\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"2017-08-16 00:00:00\",\n          \"2017-08-17 00:00:00\",\n          \"2017-08-21 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"store_nbr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 1,\n        \"max\": 54,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          27,\n          54,\n          53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"onpromotion\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20,\n        \"min\": 0,\n        \"max\": 646,\n        \"num_unique_values\": 212,\n        \"samples\": [\n          163,\n          224,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dcoilwtico\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7272154274593787,\n        \"min\": 45.96,\n        \"max\": 48.59,\n        \"num_unique_values\": 7118,\n        \"samples\": [\n          48.26753155680225,\n          48.546241234221604,\n          46.93260869565217\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"transactions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 22,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cluster\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"holiday\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 1,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 1,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 16,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"new_family\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 383
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_matrix = xgb.DMatrix(test, enable_categorical=True)\n",
        "predictions = model.predict(test_matrix)\n",
        "test['sales'] = predictions\n",
        "\n",
        "# change any negative predictions to zero\n",
        "\n",
        "test['sales'] = test['sales'].clip(lower=0)"
      ],
      "metadata": {
        "id": "0NWVgzxEs9sN"
      },
      "execution_count": 384,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test = test.drop(columns=['date', 'store_nbr', 'onpromotion', 'dcoilwtico', 'transactions',\n",
        "       'city', 'state', 'type', 'cluster', 'holiday', 'year', 'month', 'day',\n",
        "       'day_name', 'new_family'])\n",
        "\n",
        "test.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "18PHGiGtylkY",
        "outputId": "df645972-4766-4b82-eab1-bdb1640e2c0d"
      },
      "execution_count": 385,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        id       sales\n",
              "0  3000888    2.826168\n",
              "1  3000889    2.826168\n",
              "2  3000890  145.416977\n",
              "3  3000891   97.752686\n",
              "4  3000892    2.826168"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-10017543-b811-408f-bfbd-06add05f806d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3000888</td>\n",
              "      <td>2.826168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3000889</td>\n",
              "      <td>2.826168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3000890</td>\n",
              "      <td>145.416977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3000891</td>\n",
              "      <td>97.752686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3000892</td>\n",
              "      <td>2.826168</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-10017543-b811-408f-bfbd-06add05f806d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-10017543-b811-408f-bfbd-06add05f806d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-10017543-b811-408f-bfbd-06add05f806d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-4e86168b-7018-49f8-8a22-2a3827df9d7c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4e86168b-7018-49f8-8a22-2a3827df9d7c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4e86168b-7018-49f8-8a22-2a3827df9d7c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test",
              "summary": "{\n  \"name\": \"test\",\n  \"rows\": 28512,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8230,\n        \"min\": 3000888,\n        \"max\": 3029399,\n        \"num_unique_values\": 28512,\n        \"samples\": [\n          3013543,\n          3017927,\n          3003169\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 7062,\n        \"samples\": [\n          3.647075653076172,\n          148.17401123046875,\n          113.8099594116211\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 385
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test.to_csv('submission.csv', index=False)\n"
      ],
      "metadata": {
        "id": "TajLbca5y60Q"
      },
      "execution_count": 386,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sOoMEFqYy_jW"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}