PredictingCustomers.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    },
    "colab": {
      "name": "PredictingCustomers.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "c_tQSps89Hdi"
      },
      "source": [
        "import warnings\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import seaborn as sns\n",
        "sns.set_style('darkgrid')\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uKWzcvKA9Hdq",
        "outputId": "56bad54d-3aa3-446a-bbac-415190a21502"
      },
      "source": [
        "path = \"/content/sample_data/\"\n",
        "df_train = pd.read_csv(path + 'TRAIN.csv')\n",
        "df_test = pd.read_csv(path + 'TEST.csv')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (7) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xw7UuyqT9Hdr",
        "outputId": "88f07d1c-2206-44aa-f84e-b31fdd7d97dd"
      },
      "source": [
        "df_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(305163, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1635
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TgvKnoQO-v8w",
        "outputId": "7af09fbe-afd2-4fff-9c0d-177683f2b341"
      },
      "source": [
        "df_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(712046, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1636
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7gRvKBNv9Hds"
      },
      "source": [
        "## Reduce 0's from trainset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vsRAzXvh9Hds"
      },
      "source": [
        "# Reduce huge number of 0's in df_train\n",
        "\n",
        "#df_train['Sales'].value_counts()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F4NRBJ319nUq",
        "outputId": "3401c15d-29d1-4653-aec2-2fc4d52a14f8"
      },
      "source": [
        "df_train['Customers'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       122097\n",
              "560       1670\n",
              "603       1663\n",
              "539       1641\n",
              "550       1640\n",
              "         ...  \n",
              "3149         1\n",
              "4440         1\n",
              "4174         1\n",
              "4582         1\n",
              "3484         1\n",
              "Name: Customers, Length: 3956, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1638
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vMaA1ibk9Hdt"
      },
      "source": [
        "df_train = df_train.sample(frac = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zZvuEICv9Hdt"
      },
      "source": [
        "filterzeros = df_train[df_train['Customers'] == 0][:5000]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "07J6dcce9Hdt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "580d288e-5efa-4eb8-c39d-5ad1197d1185"
      },
      "source": [
        "filterzeros['Customers'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    5000\n",
              "Name: Customers, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1641
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dmu3akGi9Hdu"
      },
      "source": [
        "df_train = df_train[df_train['Customers']!=0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ggylb6pt9Hdu"
      },
      "source": [
        "xdf = pd.concat([df_train, filterzeros])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZoKrSuewCLDg"
      },
      "source": [
        "#xdf = df_train.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tTKx9Jj0h2s7",
        "outputId": "29c56719-e80d-4e32-f91d-170fd787ee07"
      },
      "source": [
        "xdf['Customers'].quantile(0.0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1645
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yo_ZJPX4VhDh",
        "outputId": "60f836d0-d5d6-47f3-d5b4-c3ed8f0cf7ed"
      },
      "source": [
        "xdf['Customers'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    594949.000000\n",
              "mean        763.540643\n",
              "std         408.360156\n",
              "min           0.000000\n",
              "25%         522.000000\n",
              "50%         679.000000\n",
              "75%         897.000000\n",
              "max        5494.000000\n",
              "Name: Customers, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1646
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rHFVjCGk9Hdu",
        "outputId": "ce68009d-ed83-44e7-e331-608049590f68"
      },
      "source": [
        "xdf['Customers'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       5000\n",
              "560     1670\n",
              "603     1663\n",
              "539     1641\n",
              "550     1640\n",
              "        ... \n",
              "3149       1\n",
              "4440       1\n",
              "4174       1\n",
              "4582       1\n",
              "3484       1\n",
              "Name: Customers, Length: 3956, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1647
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v5XspAyt9Hdu"
      },
      "source": [
        "xdf = xdf.sample(frac = 1)\n",
        "xdf = xdf.iloc[:305163]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IBt21SVo9Hdu",
        "outputId": "2633798c-46da-4760-d16c-f53f05932384"
      },
      "source": [
        "xdf['Customers'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       2578\n",
              "560      888\n",
              "576      853\n",
              "566      853\n",
              "539      850\n",
              "        ... \n",
              "3531       1\n",
              "4297       1\n",
              "3674       1\n",
              "3530       1\n",
              "3640       1\n",
              "Name: Customers, Length: 3643, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1649
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zLAD2vKw9Hdv",
        "outputId": "f45f1449-28db-4d43-b7b3-d1584b9eda09"
      },
      "source": [
        "xdf.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(305163, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1650
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1wCv42Hk9Hdv",
        "outputId": "3ceeae6a-8030-499b-c61f-3b1b035d9d03"
      },
      "source": [
        "df_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(305163, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1651
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "id": "5rzYdRFY9Hdv",
        "outputId": "7d5a585e-d095-40f6-8a42-2d9f44269f63"
      },
      "source": [
        "df_test.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Store</th>\n",
              "      <th>DayOfWeek</th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>Promo</th>\n",
              "      <th>StateHoliday</th>\n",
              "      <th>SchoolHoliday</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>767</td>\n",
              "      <td>1</td>\n",
              "      <td>2013-04-01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>b</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1111</td>\n",
              "      <td>6</td>\n",
              "      <td>2013-03-16</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>45</td>\n",
              "      <td>4</td>\n",
              "      <td>2013-08-01</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Store  DayOfWeek        Date  Open  Promo StateHoliday  SchoolHoliday\n",
              "0    767          1  2013-04-01     0      0            b              1\n",
              "1   1111          6  2013-03-16     1      0            0              0\n",
              "2     45          4  2013-08-01     1      1            0              1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1652
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZiYfQDg29Hdv"
      },
      "source": [
        "# Observe Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "kauXctGv9Hdw",
        "outputId": "9fcc0076-02d7-4d0a-cf04-cec917ab6bf2"
      },
      "source": [
        "xdf.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Store</th>\n",
              "      <th>DayOfWeek</th>\n",
              "      <th>Date</th>\n",
              "      <th>Sales</th>\n",
              "      <th>Customers</th>\n",
              "      <th>Open</th>\n",
              "      <th>Promo</th>\n",
              "      <th>StateHoliday</th>\n",
              "      <th>SchoolHoliday</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>593214</th>\n",
              "      <td>820</td>\n",
              "      <td>4</td>\n",
              "      <td>2014-01-16</td>\n",
              "      <td>8839</td>\n",
              "      <td>944</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296990</th>\n",
              "      <td>926</td>\n",
              "      <td>2</td>\n",
              "      <td>2014-10-28</td>\n",
              "      <td>5200</td>\n",
              "      <td>433</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>359001</th>\n",
              "      <td>169</td>\n",
              "      <td>5</td>\n",
              "      <td>2014-08-22</td>\n",
              "      <td>6073</td>\n",
              "      <td>609</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1071</th>\n",
              "      <td>1072</td>\n",
              "      <td>5</td>\n",
              "      <td>2015-07-31</td>\n",
              "      <td>10270</td>\n",
              "      <td>817</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>709455</th>\n",
              "      <td>1101</td>\n",
              "      <td>5</td>\n",
              "      <td>2013-10-04</td>\n",
              "      <td>10184</td>\n",
              "      <td>1054</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Store  DayOfWeek        Date  ...  Promo  StateHoliday  SchoolHoliday\n",
              "593214    820          4  2014-01-16  ...      0             0              0\n",
              "296990    926          2  2014-10-28  ...      0             0              0\n",
              "359001    169          5  2014-08-22  ...      1             0              0\n",
              "1071     1072          5  2015-07-31  ...      1             0              1\n",
              "709455   1101          5  2013-10-04  ...      0             0              1\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1653
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4OrV1H-D9Hdw"
      },
      "source": [
        "# Extract year, month and day from 'Date'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eSp9I9J09Hdw",
        "outputId": "b3dcd193-29d1-40e5-dcad-9c42250d4825"
      },
      "source": [
        "xdf['Date']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "593214    2014-01-16\n",
              "296990    2014-10-28\n",
              "359001    2014-08-22\n",
              "1071      2015-07-31\n",
              "709455    2013-10-04\n",
              "             ...    \n",
              "15275     2015-07-18\n",
              "321923    2014-10-01\n",
              "354243    2014-08-27\n",
              "640113    2013-12-05\n",
              "603476    2014-01-07\n",
              "Name: Date, Length: 305163, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1655
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W-Qa7d-O9Hdw"
      },
      "source": [
        "xdf['Date'] = pd.to_datetime(xdf['Date'])\n",
        "df_test['Date'] = pd.to_datetime(df_test['Date'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G0T3Djj49Hdx",
        "outputId": "c84b7a69-fc43-4f29-8671-8f6decc7b7f1"
      },
      "source": [
        "xdf['Date']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "593214   2014-01-16\n",
              "296990   2014-10-28\n",
              "359001   2014-08-22\n",
              "1071     2015-07-31\n",
              "709455   2013-10-04\n",
              "            ...    \n",
              "15275    2015-07-18\n",
              "321923   2014-10-01\n",
              "354243   2014-08-27\n",
              "640113   2013-12-05\n",
              "603476   2014-01-07\n",
              "Name: Date, Length: 305163, dtype: datetime64[ns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1657
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7dwUeF8x9Hdx"
      },
      "source": [
        "xdf['Year'] = xdf['Date'].dt.year\n",
        "xdf['Month'] = xdf['Date'].dt.month\n",
        "xdf['Day'] = xdf['Date'].dt.day\n",
        "\n",
        "\n",
        "df_test['Year'] = df_test['Date'].dt.year\n",
        "df_test['Month'] = df_test['Date'].dt.month\n",
        "df_test['Day'] = df_test['Date'].dt.day\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tSlsW6lD9Hdx"
      },
      "source": [
        "xdf.drop('Date', axis = 1, inplace = True)\n",
        "df_test.drop('Date', axis = 1, inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m4EMoIT49Hdx",
        "outputId": "f6e294e7-274e-4ef8-f637-993c544e4b15"
      },
      "source": [
        "xdf.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(305163, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1660
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5-gofzDo9Hdx",
        "outputId": "9fba60cf-5eb5-4c18-fabf-de8577d0463c"
      },
      "source": [
        "xdf.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 305163 entries, 593214 to 603476\n",
            "Data columns (total 11 columns):\n",
            " #   Column         Non-Null Count   Dtype \n",
            "---  ------         --------------   ----- \n",
            " 0   Store          305163 non-null  int64 \n",
            " 1   DayOfWeek      305163 non-null  int64 \n",
            " 2   Sales          305163 non-null  int64 \n",
            " 3   Customers      305163 non-null  int64 \n",
            " 4   Open           305163 non-null  int64 \n",
            " 5   Promo          305163 non-null  int64 \n",
            " 6   StateHoliday   305163 non-null  object\n",
            " 7   SchoolHoliday  305163 non-null  int64 \n",
            " 8   Year           305163 non-null  int64 \n",
            " 9   Month          305163 non-null  int64 \n",
            " 10  Day            305163 non-null  int64 \n",
            "dtypes: int64(10), object(1)\n",
            "memory usage: 27.9+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aeTxqrzP9Hdy",
        "outputId": "bd19d05c-f076-4fcd-deef-893c4d6e8c7f"
      },
      "source": [
        "xdf['StateHoliday'].values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['0', '0', 0, ..., 0, '0', '0'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1662
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ddYdTH199Hdy",
        "outputId": "ac54f98f-59d2-42e0-8a71-df34a6c7dd29"
      },
      "source": [
        "xdf['StateHoliday'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    246187\n",
              "0     58164\n",
              "a       552\n",
              "b       144\n",
              "c       116\n",
              "Name: StateHoliday, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1663
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9MFFNz-L9Hdy",
        "outputId": "7045fa26-7d47-40fd-cb0d-18978c584433"
      },
      "source": [
        "df_test['StateHoliday'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    297219\n",
              "a      5714\n",
              "b      2230\n",
              "Name: StateHoliday, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1664
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OW95EAYe9Hdy"
      },
      "source": [
        "xdf['StateHoliday'] = xdf['StateHoliday'].replace({'0':'0','a':'1','b':'1','c':'1'})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JuhuX8aV9Hdy",
        "outputId": "8722b135-2ab9-43cd-a684-432af98e3d26"
      },
      "source": [
        "xdf['StateHoliday'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    246187\n",
              "0     58164\n",
              "1       812\n",
              "Name: StateHoliday, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1666
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zfet7fEv9Hdz"
      },
      "source": [
        "df_test['StateHoliday'] = df_test['StateHoliday'].replace({'a':'1','b':'1'})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ThZ8B_pB9Hdz"
      },
      "source": [
        "xdf['StateHoliday'] = pd.to_numeric(xdf['StateHoliday'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1yXva2AV9Hdz"
      },
      "source": [
        "df_test['StateHoliday'] = pd.to_numeric(df_test['StateHoliday'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8wUbJp7j9Hdz",
        "outputId": "43dd8ced-df63-4c71-af56-4fac9dced820"
      },
      "source": [
        "# let's recheck \n",
        "\n",
        "xdf['StateHoliday'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    304351\n",
              "1       812\n",
              "Name: StateHoliday, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1670
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dE_a_yXS9Hdz",
        "outputId": "9442fed7-c45d-4058-9d25-4a3365fbdd92"
      },
      "source": [
        "df_test['StateHoliday'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    297219\n",
              "1      7944\n",
              "Name: StateHoliday, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1671
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zdHNbywa9Hdz"
      },
      "source": [
        "This need to be converted to Numeric and fixed."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-9iNIEsZ9Hd0",
        "outputId": "a28cb224-35bc-47bc-8ef8-9c4df212f70d"
      },
      "source": [
        "# school holiday\n",
        "\n",
        "xdf['SchoolHoliday'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    248948\n",
              "1     56215\n",
              "Name: SchoolHoliday, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1672
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "EQ2Rqq2o9Hd0",
        "outputId": "e945f4b6-487f-41d6-f011-9a54b03bf17f"
      },
      "source": [
        "corr = xdf.corr()\n",
        "plt.figure(figsize = (10,8))\n",
        "sns.heatmap(corr, vmax = .8, annot = True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc2ea06ef10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1673
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Wg1CDVM9Hd0"
      },
      "source": [
        "# Exploratory Data Analaysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0PUDUm9D9Hd0",
        "outputId": "ebf2955d-4d8f-433c-c9f2-0931d4082f05"
      },
      "source": [
        "xdf['Open'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    302585\n",
              "0      2578\n",
              "Name: Open, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1674
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gvofq_6l9Hd1"
      },
      "source": [
        "## Univariate (Target Attribute)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VXNdw9HX9Hd1",
        "outputId": "438a8d7c-a0be-4dd6-fdfa-e788e22fab3b"
      },
      "source": [
        "xdf['Customers'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       2578\n",
              "560      888\n",
              "576      853\n",
              "566      853\n",
              "539      850\n",
              "        ... \n",
              "3531       1\n",
              "4297       1\n",
              "3674       1\n",
              "3530       1\n",
              "3640       1\n",
              "Name: Customers, Length: 3643, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1675
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "nmUnps0u9Hd1",
        "outputId": "08108fd0-ce9f-4b4e-9754-3eff63a7c098"
      },
      "source": [
        "## Lets check its distribution\n",
        "\n",
        "sns.displot(x = 'Customers', data = xdf, aspect = 2, height = 6, kde = True);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lNJzn8uG9Hd2",
        "outputId": "e3b5b338-9e4b-4ee3-e8cf-3e968bf80992"
      },
      "source": [
        "# Skew and kurt\n",
        "print(\"Skewness: %f\" % xdf['Customers'].skew())\n",
        "print(\"Kurtosis: %f\" % xdf['Customers'].kurt())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Skewness: 2.670399\n",
            "Kurtosis: 12.614082\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "fR0V7p9U9Hd2",
        "outputId": "003cce7d-f241-4bab-d960-2b5bd23818e4"
      },
      "source": [
        "# Let's plot how Sales relates to some of the features in the dataset\n",
        "\n",
        "data = pd.concat([xdf['Customers'], xdf['StateHoliday']], axis=1)\n",
        "f, ax = plt.subplots(figsize=(8, 6))\n",
        "fig = sns.boxplot(x=xdf['StateHoliday'], y=\"Customers\", data=data)\n",
        "fig.axis(ymin=0, ymax=800000);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "PJzUa1Bv9Hd3",
        "outputId": "861354ec-cf6f-459d-882a-0f60b87cf2ad"
      },
      "source": [
        "# Let's plot how Sales relates to some of the features in the dataset\n",
        "\n",
        "data = pd.concat([xdf['Customers'], xdf['Promo']], axis=1)\n",
        "f, ax = plt.subplots(figsize=(8, 6))\n",
        "fig = sns.boxplot(x=xdf['Promo'], y=\"Customers\", data=data)\n",
        "fig.axis(ymin=0, ymax=800000);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ju3X9uw9Hd3"
      },
      "source": [
        "# Feature Engineering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "3vlp31xV9Hd4",
        "outputId": "e13daa46-211d-4a6c-f544-3d4cc54cd678"
      },
      "source": [
        "sns.set_style(\"white\")\n",
        "sns.set_color_codes(palette='deep')\n",
        "f, ax = plt.subplots(figsize=(8, 7))\n",
        "#Check the new distribution \n",
        "sns.distplot(xdf['Customers'], color=\"b\");\n",
        "ax.xaxis.grid(False)\n",
        "ax.set(ylabel=\"Frequency\")\n",
        "ax.set(xlabel=\"Customers\")\n",
        "ax.set(title=\"Customers distribution\")\n",
        "sns.despine(trim=True, left=True)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Zg2s_AG9Hd4"
      },
      "source": [
        "The Sales is skewed to the right. This is a problem because most ML models don't do well with non-normally distributed data. We can apply a log(1+x) transform to fix the skew."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rpVb4z__9Hd4"
      },
      "source": [
        "from scipy.stats import skew, norm\n",
        "import scipy.stats as stats"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NWlcPdz2YYc_",
        "outputId": "dec46358-3816-464e-b469-7ca768fb8263"
      },
      "source": [
        "xdf['Customers'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       2578\n",
              "560      888\n",
              "576      853\n",
              "566      853\n",
              "539      850\n",
              "        ... \n",
              "3531       1\n",
              "4297       1\n",
              "3674       1\n",
              "3530       1\n",
              "3640       1\n",
              "Name: Customers, Length: 3643, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1682
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eEi57zl69Hd4"
      },
      "source": [
        "xdf['Customers'] = np.log1p(xdf['Customers'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "id": "ExcN908E9Hd4",
        "outputId": "6c4e60d2-2a99-43ff-f6ed-e7ff87b79f14"
      },
      "source": [
        "sns.set_style(\"white\")\n",
        "sns.set_color_codes(palette='deep')\n",
        "f, ax = plt.subplots(figsize=(8, 7))\n",
        "#Check the new distribution \n",
        "sns.distplot(xdf['Customers'] , fit=norm, color=\"b\");\n",
        "\n",
        "# Get the fitted parameters used by the function\n",
        "(mu, sigma) = norm.fit(xdf['Customers'])\n",
        "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
        "\n",
        "#Now plot the distribution\n",
        "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\n",
        "            loc='best')\n",
        "ax.xaxis.grid(False)\n",
        "ax.set(ylabel=\"Frequency\")\n",
        "ax.set(xlabel=\"Customers\")\n",
        "ax.set(title=\"Customers distribution\")\n",
        "sns.despine(trim=True, left=True)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " mu = 6.49 and sigma = 0.74\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uoh3Dvim9Hd5",
        "outputId": "b03bce17-68cc-4481-d1ce-0edc5ef6f68c"
      },
      "source": [
        "xdf.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(305163, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1685
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tl6jGOOU9Hd5"
      },
      "source": [
        "## Correlation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "bnoxl7FQ9Hd5",
        "outputId": "b929619b-db0e-4441-baa7-dd58e0d9a64f"
      },
      "source": [
        "# Let's check the correlation of the feature with target\n",
        "\n",
        "corr = xdf.corr()\n",
        "\n",
        "plt.figure(figsize = (10,8))\n",
        "sns.heatmap(corr, vmax = .8, annot = True);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OShCfTOG9Hd5"
      },
      "source": [
        "xdf.drop(['Sales','DayOfWeek','StateHoliday'], axis = 1, inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YFlgwhc29Hd6"
      },
      "source": [
        "df_test.drop(['DayOfWeek','StateHoliday'], axis = 1, inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "mWnzDEfPS-s0",
        "outputId": "fa60f6da-490f-464c-9825-8621452941d2"
      },
      "source": [
        "# Let's check the correlation of the feature with target\n",
        "\n",
        "corr = xdf.corr()\n",
        "\n",
        "plt.figure(figsize = (10,8))\n",
        "sns.heatmap(corr, vmax = .8, annot = True);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bd22U0QP9Hd6"
      },
      "source": [
        "#### Fix skewed features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5jxBNS4q9Hd7"
      },
      "source": [
        "# Dataset Preparation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p3iqIv9c9Hd7"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lnovtw6k9Hd7"
      },
      "source": [
        "# Splitting the dataset\n",
        "\n",
        "X = xdf.drop(['Customers'], axis =1)\n",
        "y = xdf['Customers']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yD5OvSpf9Hd8"
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DIIkJRxi9Hd8"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.1, random_state = 42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KdcTh7j39Hd8"
      },
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.pipeline import Pipeline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6cVg0ka49Hd8",
        "outputId": "13d05483-5192-44b7-fe83-4be9102d0077"
      },
      "source": [
        "(X_train.shape, X_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((274646, 7), (30517, 7))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1694
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "L64H2ytb9Hd8",
        "outputId": "68603fce-0428-491f-a31e-34b08a2020f9"
      },
      "source": [
        "X_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Store</th>\n",
              "      <th>Open</th>\n",
              "      <th>Promo</th>\n",
              "      <th>SchoolHoliday</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>358228</th>\n",
              "      <td>359</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2014</td>\n",
              "      <td>8</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>523900</th>\n",
              "      <td>636</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2014</td>\n",
              "      <td>3</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23713</th>\n",
              "      <td>299</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2015</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>313543</th>\n",
              "      <td>589</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2014</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>703565</th>\n",
              "      <td>786</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2013</td>\n",
              "      <td>10</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>459536</th>\n",
              "      <td>942</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2014</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>206850</th>\n",
              "      <td>576</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2015</td>\n",
              "      <td>1</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>602666</th>\n",
              "      <td>237</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2014</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>287311</th>\n",
              "      <td>527</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2014</td>\n",
              "      <td>11</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173943</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2015</td>\n",
              "      <td>2</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>274646 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Store  Open  Promo  SchoolHoliday  Year  Month  Day\n",
              "358228    359     1      0              0  2014      8   23\n",
              "523900    636     1      1              0  2014      3   19\n",
              "23713     299     1      0              0  2015      7   10\n",
              "313543    589     1      1              0  2014     10   10\n",
              "703565    786     1      1              0  2013     10    9\n",
              "...       ...   ...    ...            ...   ...    ...  ...\n",
              "459536    942     1      0              0  2014      5   16\n",
              "206850    576     1      1              0  2015      1   27\n",
              "602666    237     1      1              0  2014      1    7\n",
              "287311    527     1      1              0  2014     11    7\n",
              "173943      4     1      0              0  2015      2   25\n",
              "\n",
              "[274646 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1695
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "ER3mhZRmICM5",
        "outputId": "758f2e72-66ec-4628-d5b2-3e46a8f83617"
      },
      "source": [
        "df_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Store</th>\n",
              "      <th>Open</th>\n",
              "      <th>Promo</th>\n",
              "      <th>SchoolHoliday</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>767</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2013</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1111</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2013</td>\n",
              "      <td>3</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2013</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>500</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2013</td>\n",
              "      <td>5</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2013</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305158</th>\n",
              "      <td>412</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2013</td>\n",
              "      <td>5</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305159</th>\n",
              "      <td>936</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2013</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305160</th>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2013</td>\n",
              "      <td>9</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305161</th>\n",
              "      <td>1077</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2013</td>\n",
              "      <td>6</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305162</th>\n",
              "      <td>321</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2013</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>305163 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Store  Open  Promo  SchoolHoliday  Year  Month  Day\n",
              "0         767     0      0              1  2013      4    1\n",
              "1        1111     1      0              0  2013      3   16\n",
              "2          45     1      1              1  2013      8    1\n",
              "3         500     1      0              1  2013      5   23\n",
              "4          82     1      0              1  2013      8    6\n",
              "...       ...   ...    ...            ...   ...    ...  ...\n",
              "305158    412     1      1              1  2013      5   28\n",
              "305159    936     1      0              0  2013      3    2\n",
              "305160     21     1      0              0  2013      9   16\n",
              "305161   1077     1      0              0  2013      6   22\n",
              "305162    321     1      0              0  2013      9    7\n",
              "\n",
              "[305163 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1696
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Puxc5Cgz9Hd8"
      },
      "source": [
        "### Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6yVy_eSG9Hd9"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S62043Cd9Hd9"
      },
      "source": [
        "ct = ColumnTransformer([\n",
        "                ('scaler', StandardScaler(),['Store','Month','Day'])\n",
        "], remainder = 'passthrough')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PdokxMjj9Hd9"
      },
      "source": [
        "# Modeling and Evaluation Metrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p73pwe359Hd9"
      },
      "source": [
        "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
        "import math"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i4vesWaw9Hd9"
      },
      "source": [
        "## Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q1POwDJ49Hd9"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1rtmUW0O9Hd-"
      },
      "source": [
        "lr = LinearRegression()\n",
        "lr_pipe = Pipeline([\n",
        "            ('column-transformer', ct),\n",
        "            ('LinearRegression', lr)\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6p-GxyxJ9Hd-",
        "outputId": "8b866464-1aa2-46e3-a2ba-1f51154f4e7f"
      },
      "source": [
        "lr_pipe.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('column-transformer',\n",
              "                 ColumnTransformer(n_jobs=None, remainder='passthrough',\n",
              "                                   sparse_threshold=0.3,\n",
              "                                   transformer_weights=None,\n",
              "                                   transformers=[('scaler',\n",
              "                                                  StandardScaler(copy=True,\n",
              "                                                                 with_mean=True,\n",
              "                                                                 with_std=True),\n",
              "                                                  ['Store', 'Month', 'Day'])],\n",
              "                                   verbose=False)),\n",
              "                ('LinearRegression',\n",
              "                 LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
              "                                  normalize=False))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1702
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rpwi8o5g9Hd-"
      },
      "source": [
        "lr_pred = lr_pipe.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_qwI7U8k9Hd-",
        "outputId": "a31430eb-cefb-4bcd-8826-af165c450c4e"
      },
      "source": [
        "print(\"R2 Score:\", r2_score(y_test, lr_pred))\n",
        "print(\"Mean Squarred Error: %.3f\" % mean_squared_error(y_test, lr_pred))\n",
        "print(\"RMSE:\", math.sqrt(mean_squared_error(y_test, lr_pred)))\n",
        "print(\"Mean Absolute Error : \" + str(mean_absolute_error(y_test,lr_pred)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R2 Score: 0.677528368062964\n",
            "Mean Squarred Error: 0.175\n",
            "RMSE: 0.4182784423561084\n",
            "Mean Absolute Error : 0.3182166337820209\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h4pVpZm89Hd-"
      },
      "source": [
        "## GradientBoostingRegressor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "INODifQf9Hd-"
      },
      "source": [
        "from sklearn.ensemble import GradientBoostingRegressor\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MYkwORXn9Hd_"
      },
      "source": [
        "gbr = GradientBoostingRegressor()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TpD4VSyD9Hd_"
      },
      "source": [
        "gbr_pipe = Pipeline([\n",
        "            ('column-transformer', ct),\n",
        "            ('GradientBoostingRegressor', gbr)\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A5doSOtx9Hd_",
        "outputId": "f2a0d205-f4cc-439f-9e7a-ecc842f29022"
      },
      "source": [
        "gbr_pipe.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('column-transformer',\n",
              "                 ColumnTransformer(n_jobs=None, remainder='passthrough',\n",
              "                                   sparse_threshold=0.3,\n",
              "                                   transformer_weights=None,\n",
              "                                   transformers=[('scaler',\n",
              "                                                  StandardScaler(copy=True,\n",
              "                                                                 with_mean=True,\n",
              "                                                                 with_std=True),\n",
              "                                                  ['Store', 'Month', 'Day'])],\n",
              "                                   verbose=False)),\n",
              "                ('GradientBoostingRegressor',\n",
              "                 GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0,...\n",
              "                                           learning_rate=0.1, loss='ls',\n",
              "                                           max_depth=3, max_features=None,\n",
              "                                           max_leaf_nodes=None,\n",
              "                                           min_impurity_decrease=0.0,\n",
              "                                           min_impurity_split=None,\n",
              "                                           min_samples_leaf=1,\n",
              "                                           min_samples_split=2,\n",
              "                                           min_weight_fraction_leaf=0.0,\n",
              "                                           n_estimators=100,\n",
              "                                           n_iter_no_change=None,\n",
              "                                           presort='deprecated',\n",
              "                                           random_state=None, subsample=1.0,\n",
              "                                           tol=0.0001, validation_fraction=0.1,\n",
              "                                           verbose=0, warm_start=False))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1708
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K59IRpWF9Hd_"
      },
      "source": [
        "gbr_pred = gbr_pipe.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l6024c2P9Hd_",
        "outputId": "71add288-c3eb-42ff-efe3-fe86c562bc64"
      },
      "source": [
        "print(\"R2 Score:\", r2_score(y_test, gbr_pred))\n",
        "print(\"Mean Squarred Error:\", mean_squared_error(y_test, gbr_pred))\n",
        "print(\"RMSE:\", math.sqrt(mean_squared_error(y_test, gbr_pred)))\n",
        "print(\"Mean Absolute Error : \" + str(mean_absolute_error(y_test,gbr_pred)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R2 Score: 0.7200656877519986\n",
            "Mean Squarred Error: 0.15187824950195147\n",
            "RMSE: 0.38971560079364476\n",
            "Mean Absolute Error : 0.29945111797113\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AQs5vxcz9Hd_"
      },
      "source": [
        "## XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ww_swSsb9HeA"
      },
      "source": [
        "from xgboost import XGBRegressor"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LkjEY0D29HeA"
      },
      "source": [
        "xgb = XGBRegressor(n_estimators = 100, learning_rate = 0.05)\n",
        "xgb_pipe = Pipeline([\n",
        "            ('column-transformer', ct),\n",
        "            ('XGBRegression', xgb)\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4l0SuwF49HeA",
        "outputId": "67f97bd1-e565-4775-933a-b6e3f61f527c"
      },
      "source": [
        "xgb_pipe.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[04:57:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('column-transformer',\n",
              "                 ColumnTransformer(n_jobs=None, remainder='passthrough',\n",
              "                                   sparse_threshold=0.3,\n",
              "                                   transformer_weights=None,\n",
              "                                   transformers=[('scaler',\n",
              "                                                  StandardScaler(copy=True,\n",
              "                                                                 with_mean=True,\n",
              "                                                                 with_std=True),\n",
              "                                                  ['Store', 'Month', 'Day'])],\n",
              "                                   verbose=False)),\n",
              "                ('XGBRegression',\n",
              "                 XGBRegressor(base_score=0.5, booster='gbtree',\n",
              "                              colsample_bylevel=1, colsample_bynode=1,\n",
              "                              colsample_bytree=1, gamma=0,\n",
              "                              importance_type='gain', learning_rate=0.05,\n",
              "                              max_delta_step=0, max_depth=3, min_child_weight=1,\n",
              "                              missing=None, n_estimators=100, n_jobs=1,\n",
              "                              nthread=None, objective='reg:linear',\n",
              "                              random_state=0, reg_alpha=0, reg_lambda=1,\n",
              "                              scale_pos_weight=1, seed=None, silent=None,\n",
              "                              subsample=1, verbosity=1))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1713
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ymq2qgJr9HeA"
      },
      "source": [
        "xgb_pred = xgb_pipe.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EtXhRa-V9HeA",
        "outputId": "2066e814-935d-46f3-aaca-3ece019743b7"
      },
      "source": [
        "print(\"R2 Score:\", r2_score(y_test, xgb_pred))\n",
        "print(\"Mean Squarred Error: %.3f \" % mean_squared_error(y_test, xgb_pred))\n",
        "print(\"RMSE:\", math.sqrt(mean_squared_error(y_test, xgb_pred)))\n",
        "print(\"Mean Absolute Error : \" + str(mean_absolute_error(y_test,xgb_pred)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R2 Score: 0.691569878111421\n",
            "Mean Squarred Error: 0.167 \n",
            "RMSE: 0.4090704580647829\n",
            "Mean Absolute Error : 0.3113254043452319\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-8J4fyrU9HeA"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OviUjnX_9HeB"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YMK77q1z9HeB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "88e2ccbd-8a7a-4dc9-9cd6-a1449b6cd7b0"
      },
      "source": [
        "X_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Store</th>\n",
              "      <th>Open</th>\n",
              "      <th>Promo</th>\n",
              "      <th>SchoolHoliday</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>346719</th>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2014</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18905</th>\n",
              "      <td>1066</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2015</td>\n",
              "      <td>7</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70899</th>\n",
              "      <td>655</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2015</td>\n",
              "      <td>5</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47080</th>\n",
              "      <td>251</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2015</td>\n",
              "      <td>6</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192676</th>\n",
              "      <td>897</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2015</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80724</th>\n",
              "      <td>445</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2015</td>\n",
              "      <td>5</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565982</th>\n",
              "      <td>348</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2014</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>386584</th>\n",
              "      <td>729</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2014</td>\n",
              "      <td>7</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>541480</th>\n",
              "      <td>376</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2014</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52353</th>\n",
              "      <td>1064</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2015</td>\n",
              "      <td>6</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>30517 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Store  Open  Promo  SchoolHoliday  Year  Month  Day\n",
              "346719     10     1      1              1  2014      9    4\n",
              "18905    1066     1      1              1  2015      7   15\n",
              "70899     655     1      0              0  2015      5   29\n",
              "47080     251     1      1              0  2015      6   19\n",
              "192676    897     1      0              0  2015      2    9\n",
              "...       ...   ...    ...            ...   ...    ...  ...\n",
              "80724     445     1      1              0  2015      5   20\n",
              "565982    348     0      0              0  2014      2    9\n",
              "386584    729     1      0              1  2014      7   24\n",
              "541480    376     1      1              0  2014      3    3\n",
              "52353    1064     1      1              0  2015      6   15\n",
              "\n",
              "[30517 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1718
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-68nyK3k9HeB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "b58dadcd-867c-4959-91e2-6e9d79398161"
      },
      "source": [
        "df_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Store</th>\n",
              "      <th>Open</th>\n",
              "      <th>Promo</th>\n",
              "      <th>SchoolHoliday</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>767</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2013</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1111</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2013</td>\n",
              "      <td>3</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2013</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>500</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2013</td>\n",
              "      <td>5</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2013</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305158</th>\n",
              "      <td>412</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2013</td>\n",
              "      <td>5</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305159</th>\n",
              "      <td>936</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2013</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305160</th>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2013</td>\n",
              "      <td>9</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305161</th>\n",
              "      <td>1077</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2013</td>\n",
              "      <td>6</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305162</th>\n",
              "      <td>321</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2013</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>305163 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Store  Open  Promo  SchoolHoliday  Year  Month  Day\n",
              "0         767     0      0              1  2013      4    1\n",
              "1        1111     1      0              0  2013      3   16\n",
              "2          45     1      1              1  2013      8    1\n",
              "3         500     1      0              1  2013      5   23\n",
              "4          82     1      0              1  2013      8    6\n",
              "...       ...   ...    ...            ...   ...    ...  ...\n",
              "305158    412     1      1              1  2013      5   28\n",
              "305159    936     1      0              0  2013      3    2\n",
              "305160     21     1      0              0  2013      9   16\n",
              "305161   1077     1      0              0  2013      6   22\n",
              "305162    321     1      0              0  2013      9    7\n",
              "\n",
              "[305163 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1717
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FYyn-qkk9HeB"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xisg4idO9HeB"
      },
      "source": [
        "output = pd.DataFrame(columns = ['Index','Customers'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UR4MU4lY9HeB"
      },
      "source": [
        "output['Index'] = df_test.index"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "um0MhJ-m9HeB"
      },
      "source": [
        "df_test['Customers'] = np.floor(np.expm1(gbr_pipe.predict(df_test)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "37eBd9Hcbh1r",
        "outputId": "b338fae0-5b09-4df8-ec40-035b23044010"
      },
      "source": [
        "df_test['Customers'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0       47910\n",
              "620.0      4189\n",
              "628.0      3872\n",
              "629.0      3562\n",
              "619.0      3361\n",
              "          ...  \n",
              "1204.0        1\n",
              "460.0         1\n",
              "2175.0        1\n",
              "1328.0        1\n",
              "1484.0        1\n",
              "Name: Customers, Length: 911, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1728
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YK5x14Sa9HeB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "cdf4b46c-869d-4a96-e404-85287f3154c1"
      },
      "source": [
        "df_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Store</th>\n",
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              "      <th>Promo</th>\n",
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              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>Customers</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>2013</td>\n",
              "      <td>3</td>\n",
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              "      <td>484.0</td>\n",
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              "      <th>2</th>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2013</td>\n",
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              "      <td>1</td>\n",
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              "      <td>662.0</td>\n",
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              "      <td>82</td>\n",
              "      <td>1</td>\n",
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              "      <td>2013</td>\n",
              "      <td>8</td>\n",
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              "      <td>619.0</td>\n",
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              "    <tr>\n",
              "      <th>305158</th>\n",
              "      <td>412</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2013</td>\n",
              "      <td>5</td>\n",
              "      <td>28</td>\n",
              "      <td>775.0</td>\n",
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              "    <tr>\n",
              "      <th>305159</th>\n",
              "      <td>936</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2013</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>644.0</td>\n",
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              "    <tr>\n",
              "      <th>305160</th>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>9</td>\n",
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              "      <td>581.0</td>\n",
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              "    <tr>\n",
              "      <th>305161</th>\n",
              "      <td>1077</td>\n",
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              "      <td>627.0</td>\n",
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              "      <th>305162</th>\n",
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              "      <td>588.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>305163 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Store  Open  Promo  SchoolHoliday  Year  Month  Day  Customers\n",
              "0         767     0      0              1  2013      4    1        0.0\n",
              "1        1111     1      0              0  2013      3   16      484.0\n",
              "2          45     1      1              1  2013      8    1      729.0\n",
              "3         500     1      0              1  2013      5   23      662.0\n",
              "4          82     1      0              1  2013      8    6      619.0\n",
              "...       ...   ...    ...            ...   ...    ...  ...        ...\n",
              "305158    412     1      1              1  2013      5   28      775.0\n",
              "305159    936     1      0              0  2013      3    2      644.0\n",
              "305160     21     1      0              0  2013      9   16      581.0\n",
              "305161   1077     1      0              0  2013      6   22      627.0\n",
              "305162    321     1      0              0  2013      9    7      588.0\n",
              "\n",
              "[305163 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1730
        }
      ]
    },
    {
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      "outputs": [
        {
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          "data": {
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              "<p>589949 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Store  DayOfWeek        Date  ...  Promo  StateHoliday  SchoolHoliday\n",
              "329551    731          2  2014-09-23  ...      0             0              0\n",
              "607465    576          5  2014-01-03  ...      0             0              1\n",
              "109865    596          5  2015-04-24  ...      0             0              0\n",
              "457       458          5  2015-07-31  ...      1             0              1\n",
              "419030    576          6  2014-06-21  ...      0             0              0\n",
              "...       ...        ...         ...  ...    ...           ...            ...\n",
              "411525    876          6  2014-06-28  ...      0             0              0\n",
              "62865     426          5  2015-06-05  ...      1             0              0\n",
              "123813     49          6  2015-04-11  ...      0             0              0\n",
              "2985      756          3  2015-07-29  ...      1             0              1\n",
              "613809    230          6  2013-12-28  ...      0             0              0\n",
              "\n",
              "[589949 rows x 9 columns]"
            ]
          },
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          "execution_count": 1735
        }
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    {
      "cell_type": "code",
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        "id": "XHmlf0DybFw1"
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      "source": [
        ""
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      "execution_count": null,
      "outputs": []
    },
    {
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      },
      "source": [
        "output['Customers'].apply(numpy_isqrt)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         3.010362e-01\n",
              "1         7.668051e-06\n",
              "2         8.437948e-07\n",
              "3         2.281446e-06\n",
              "4         3.370716e-06\n",
              "              ...     \n",
              "305158    7.201448e-07\n",
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              "305160    3.586433e-06\n",
              "305161    2.716484e-06\n",
              "305162    3.091862e-06\n",
              "Name: Customers, Length: 305163, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 314
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "id": "gLzZ_EkyDaZd",
        "outputId": "752f2d67-3fce-4c12-d711-36239284ee4c"
      },
      "source": [
        "df_test['Customers'] = np.floor(np.expm1(xgb_pipe.predict(df_test)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-104-9e32c92a722c>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    df_test['Customers'] = np.floor(np.expm1(xgb_pipe.predict(df_test)))b\u001b[0m\n\u001b[0m                                                                        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
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        "id": "q1frVLorUwLy",
        "outputId": "3180a507-8e38-4bcc-c115-0717d82a6cfe"
      },
      "source": [
        "df_train[df_train['Open']==0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>a</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>710584</th>\n",
              "      <td>1115</td>\n",
              "      <td>4</td>\n",
              "      <td>2013-10-03</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>a</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>122063 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Store  DayOfWeek        Date  ...  Promo  StateHoliday  SchoolHoliday\n",
              "291       292          5  2015-07-31  ...      1             0              1\n",
              "875       876          5  2015-07-31  ...      1             0              1\n",
              "1406      292          4  2015-07-30  ...      1             0              1\n",
              "1990      876          4  2015-07-30  ...      1             0              1\n",
              "2521      292          3  2015-07-29  ...      1             0              1\n",
              "...       ...        ...         ...  ...    ...           ...            ...\n",
              "710580   1111          4  2013-10-03  ...      0             a              0\n",
              "710581   1112          4  2013-10-03  ...      0             a              0\n",
              "710582   1113          4  2013-10-03  ...      0             a              0\n",
              "710583   1114          4  2013-10-03  ...      0             a              1\n",
              "710584   1115          4  2013-10-03  ...      0             a              0\n",
              "\n",
              "[122063 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "qh83AsJCUdZ7",
        "outputId": "d439a3f1-6150-4ae2-c283-c440af073045"
      },
      "source": [
        "df_test[df_test['Open'] == 0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>22</th>\n",
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              "      <th>29</th>\n",
              "      <td>22</td>\n",
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              "      <td>0</td>\n",
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              "      <td>27</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>305115</th>\n",
              "      <td>320</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <th>305138</th>\n",
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              "      <td>0</td>\n",
              "      <td>2013</td>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>305157</th>\n",
              "      <td>219</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2013</td>\n",
              "      <td>4</td>\n",
              "      <td>28</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>50754 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Store  Open  Promo  StateHoliday  ...  Year  Month  Day  Customers\n",
              "0         767     0      0             1  ...  2013      4    1        0.0\n",
              "5         402     0      0             0  ...  2013      2   17        0.0\n",
              "7         255     0      0             0  ...  2013      4   14        0.0\n",
              "22        137     0      0             0  ...  2013      4   14        0.0\n",
              "29         22     0      0             0  ...  2013      4   14        0.0\n",
              "...       ...   ...    ...           ...  ...   ...    ...  ...        ...\n",
              "305106    213     0      0             0  ...  2013      1   27        0.0\n",
              "305115    320     0      0             0  ...  2013      4    7        0.0\n",
              "305128    915     0      0             0  ...  2013      3   17        0.0\n",
              "305138    857     0      0             0  ...  2013      1   20        0.0\n",
              "305157    219     0      0             0  ...  2013      4   28        0.0\n",
              "\n",
              "[50754 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1MwjTkRx9HeC"
      },
      "source": [
        "df_test.to_csv('custom_df_test.csv', index = False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2vuvdV8Q9HeC"
      },
      "source": [
        "mysub= pd.read_csv('customers.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "Hn6YM4609HeC",
        "outputId": "08a8518e-bf6a-4f5e-e118-cdd3cead7f53"
      },
      "source": [
        "mysub"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
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              "        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>Index</th>\n",
              "      <th>Customers</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>516.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>748.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>625.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>595.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305158</th>\n",
              "      <td>305158</td>\n",
              "      <td>745.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305159</th>\n",
              "      <td>305159</td>\n",
              "      <td>619.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305160</th>\n",
              "      <td>305160</td>\n",
              "      <td>584.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305161</th>\n",
              "      <td>305161</td>\n",
              "      <td>612.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305162</th>\n",
              "      <td>305162</td>\n",
              "      <td>593.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>305163 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         Index  Customers\n",
              "0            0        0.0\n",
              "1            1      516.0\n",
              "2            2      748.0\n",
              "3            3      625.0\n",
              "4            4      595.0\n",
              "...        ...        ...\n",
              "305158  305158      745.0\n",
              "305159  305159      619.0\n",
              "305160  305160      584.0\n",
              "305161  305161      612.0\n",
              "305162  305162      593.0\n",
              "\n",
              "[305163 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 388
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lwcNo_RBDjsP"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}