supervised/notebooks/baseline.ipynb

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      "metadata": {
        "id": "LtKm9EcR-o4K"
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      "source": [
        "# Diplodatos Kaggle Competition\n",
        "---\n",
        "Grupo 26\n",
        "\n",
        "- Alexander Joel Vivas Montolla\n",
        "- Carlos Barcia\n",
        "- Nicolas E Ponce\n",
        "- Sergio Sulca\n",
        "---\n",
        "\n",
        "We present this peace of code to create the baseline for the competition, and as an example of how to deal with these kind of problems. The main goals are that you:\n",
        "\n",
        "1. Learn\n",
        "1. Try different models and see which one fits the best the given data\n",
        "1. Get a higher score than the given one in the current baseline example\n",
        "1. Try to get the highest score in the class :)\n",
        "\n",
        "\n",
        "\n",
        "Data fields\n",
        "\n",
        "* `TripType` - a categorical id representing the type of shopping trip the customer made. This is the ground truth that you are predicting. TripType_999 is an \"other\" category.\n",
        "\n",
        "* `VisitNumber` - an id corresponding to a single trip by a single customer\n",
        "\n",
        "* `Weekday` - the weekday of the trip\n",
        "* `Upc` - the UPC number of the product purchased\n",
        "* `ScanCount` - the number of the given item that was purchased. A negative value indicates a product return.\n",
        "\n",
        "* `DepartmentDescription` - a high-level description of the item's department\n",
        "\n",
        "* `FinelineNumber` - a more refined category for each of the products, created by Walmart"
      ]
    },
    {
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "!pip install xgboost"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: xgboost in /usr/local/lib/python3.6/dist-packages (0.90)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from xgboost) (1.18.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from xgboost) (1.4.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uSAF9Cee-o4L",
        "outputId": "fe3f957b-0e56-40f7-a5bd-2d3543ef3b31",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "# Import the required packages\n",
        "import os\n",
        "import math\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "from sklearn.tree import DecisionTreeClassifier as DT\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import plot_confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "\n",
        "\n",
        "from scipy.stats import uniform, truncnorm, randint"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fTsGaMvQmLBv"
      },
      "source": [
        "# variables.\n",
        "file_path_train = 'https://raw.githubusercontent.com/DiploDatos/AprendizajeSupervisado/master/practico/data/train.csv'\n",
        "file_path_test = 'https://raw.githubusercontent.com/DiploDatos/AprendizajeSupervisado/master/practico/data/test.csv'\n",
        "\n",
        "dtype={\n",
        "        #'TripType': np.uint8, # unsigned number\n",
        "        #'VisitNumber': np.uint32,\n",
        "        #'Weekday': str,\n",
        "        'Upc': str,\n",
        "        #'ScanCount': np.int32,\n",
        "        #'DepartmentDescription': str,\n",
        "        #'FinelineNumber': str # long\n",
        "        }"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qPwzQuB_-o4N"
      },
      "source": [
        "Read the *original* dataset..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rHGWvNN9-o4O",
        "outputId": "650d8f86-40b4-4a10-c4eb-86fc139737e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155
        }
      },
      "source": [
        "original_df = pd.read_csv(file_path_train, dtype=dtype)\n",
        "\n",
        "original_df.dtypes"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TripType                   int64\n",
              "VisitNumber                int64\n",
              "Weekday                   object\n",
              "Upc                       object\n",
              "ScanCount                  int64\n",
              "DepartmentDescription     object\n",
              "FinelineNumber           float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a9p3lDUfzUk3"
      },
      "source": [
        "Looking into the columns values..."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CkIEC_d8-o4T"
      },
      "source": [
        "**TripType** is the column that we should predict. That column is not present\n",
        "in the test set.\n",
        "\n",
        "The min value in `ScanCount` column is `-10`, but a negative value indicates a \n",
        "product return. It is posible make a new column using if a values is negative \n",
        "or not."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RCLxfaaI-o4U",
        "outputId": "4f2ca28c-7022-4d62-f40b-b4ef8d35de1c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        }
      },
      "source": [
        "original_df.describe(include='all')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TripType</th>\n",
              "      <th>VisitNumber</th>\n",
              "      <th>Weekday</th>\n",
              "      <th>Upc</th>\n",
              "      <th>ScanCount</th>\n",
              "      <th>DepartmentDescription</th>\n",
              "      <th>FinelineNumber</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>453411.000000</td>\n",
              "      <td>453411.000000</td>\n",
              "      <td>453411</td>\n",
              "      <td>450559</td>\n",
              "      <td>453411.000000</td>\n",
              "      <td>452439</td>\n",
              "      <td>450559.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7</td>\n",
              "      <td>84491</td>\n",
              "      <td>NaN</td>\n",
              "      <td>68</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>4011.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>GROCERY DRY GOODS</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>94602</td>\n",
              "      <td>5355</td>\n",
              "      <td>NaN</td>\n",
              "      <td>49134</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>58.027039</td>\n",
              "      <td>95909.846115</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.108584</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3727.366554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>155.973370</td>\n",
              "      <td>55399.801147</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.707029</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2779.958546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>27.000000</td>\n",
              "      <td>49390.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1404.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>39.000000</td>\n",
              "      <td>96781.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3352.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>40.000000</td>\n",
              "      <td>143930.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5501.000000</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>999.000000</td>\n",
              "      <td>191347.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>NaN</td>\n",
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              "  </tbody>\n",
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            ],
            "text/plain": [
              "             TripType    VisitNumber  ... DepartmentDescription FinelineNumber\n",
              "count   453411.000000  453411.000000  ...                452439  450559.000000\n",
              "unique            NaN            NaN  ...                    68            NaN\n",
              "top               NaN            NaN  ...     GROCERY DRY GOODS            NaN\n",
              "freq              NaN            NaN  ...                 49134            NaN\n",
              "mean        58.027039   95909.846115  ...                   NaN    3727.366554\n",
              "std        155.973370   55399.801147  ...                   NaN    2779.958546\n",
              "min          3.000000       5.000000  ...                   NaN       0.000000\n",
              "25%         27.000000   49390.000000  ...                   NaN    1404.000000\n",
              "50%         39.000000   96781.000000  ...                   NaN    3352.000000\n",
              "75%         40.000000  143930.000000  ...                   NaN    5501.000000\n",
              "max        999.000000  191347.000000  ...                   NaN    9998.000000\n",
              "\n",
              "[11 rows x 7 columns]"
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        }
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      "metadata": {
        "id": "ZlJInshpzUk9",
        "outputId": "28497fab-a654-4b11-d1d8-a51144ef1118",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "original_df.Weekday.nunique(dropna=False)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nGhzx_cN-o4b",
        "outputId": "7004c54f-d725-45f0-87a4-3d3e543fda37",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "original_df.DepartmentDescription.nunique(dropna=False)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "69"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SrW4yf2vzUlD",
        "outputId": "65de0e28-b095-41f1-93ea-7e5e685e5e9d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "original_df.FinelineNumber.nunique(dropna=False)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5106"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "_v0KTA3LzUlF",
        "outputId": "123d48e1-2962-4851-ff4b-320635e90537",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "original_df.Upc.nunique(dropna=False)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "84492"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mmXTzsN3c8oT"
      },
      "source": [
        "## 1 Pre-processing\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f_S77OnK-o4g"
      },
      "source": [
        "### 1.1 `NaN` values\n",
        "\n",
        "There are `nan`s in the column, let us find them..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yqTfAluFb6Qz",
        "outputId": "40babf20-59cf-4296-b783-54a03e874208",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155
        }
      },
      "source": [
        "original_df.isna().sum()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TripType                    0\n",
              "VisitNumber                 0\n",
              "Weekday                     0\n",
              "Upc                      2852\n",
              "ScanCount                   0\n",
              "DepartmentDescription     972\n",
              "FinelineNumber           2852\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s1QWClt4-o4g",
        "outputId": "ccc79871-787d-4912-bb33-dcfca3222e75",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "original_df[original_df.DepartmentDescription.isna()]"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>450036</th>\n",
              "      <td>999</td>\n",
              "      <td>190182</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>450163</th>\n",
              "      <td>40</td>\n",
              "      <td>190230</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>450361</th>\n",
              "      <td>39</td>\n",
              "      <td>190309</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>450595</th>\n",
              "      <td>41</td>\n",
              "      <td>190408</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>972 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        TripType  VisitNumber  ... DepartmentDescription FinelineNumber\n",
              "739          999          484  ...                   NaN            NaN\n",
              "740          999          484  ...                   NaN            NaN\n",
              "1292          32          845  ...                   NaN            NaN\n",
              "1293          32          845  ...                   NaN            NaN\n",
              "1577          40         1004  ...                   NaN            NaN\n",
              "...          ...          ...  ...                   ...            ...\n",
              "449840        40       190133  ...                   NaN            NaN\n",
              "450036       999       190182  ...                   NaN            NaN\n",
              "450163        40       190230  ...                   NaN            NaN\n",
              "450361        39       190309  ...                   NaN            NaN\n",
              "450595        41       190408  ...                   NaN            NaN\n",
              "\n",
              "[972 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "hideCode": true,
        "id": "b5O8hX4h-o4i"
      },
      "source": [
        "When the description is `NaN`, then the Upc and FinelineNumber are both NaN?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wdikGH86-o4j",
        "outputId": "19442c24-3cb1-49a8-a479-40e0d3ce2e64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "(\n",
        "    original_df.DepartmentDescription.isna().sum(),\n",
        "    (original_df.DepartmentDescription.isna() & \n",
        "     original_df.Upc.isna() & \n",
        "     original_df.FinelineNumber.isna()).sum())"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(972, 972)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ykJ6mW09-o4l",
        "outputId": "52561e50-d7dc-4a5c-ebdf-52afb24e565b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "original_df[original_df.Upc.isna()]"
      ],
      "execution_count": 14,
      "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>TripType</th>\n",
              "      <th>VisitNumber</th>\n",
              "      <th>Weekday</th>\n",
              "      <th>Upc</th>\n",
              "      <th>ScanCount</th>\n",
              "      <th>DepartmentDescription</th>\n",
              "      <th>FinelineNumber</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>739</th>\n",
              "      <td>999</td>\n",
              "      <td>484</td>\n",
              "      <td>Friday</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>740</th>\n",
              "      <td>999</td>\n",
              "      <td>484</td>\n",
              "      <td>Friday</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>900</th>\n",
              "      <td>5</td>\n",
              "      <td>585</td>\n",
              "      <td>Friday</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>PHARMACY RX</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>944</th>\n",
              "      <td>5</td>\n",
              "      <td>619</td>\n",
              "      <td>Friday</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>PHARMACY RX</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>945</th>\n",
              "      <td>5</td>\n",
              "      <td>619</td>\n",
              "      <td>Friday</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>PHARMACY RX</td>\n",
              "      <td>NaN</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>449840</th>\n",
              "      <td>40</td>\n",
              "      <td>190133</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>450036</th>\n",
              "      <td>999</td>\n",
              "      <td>190182</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>450163</th>\n",
              "      <td>40</td>\n",
              "      <td>190230</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>450361</th>\n",
              "      <td>39</td>\n",
              "      <td>190309</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>450595</th>\n",
              "      <td>41</td>\n",
              "      <td>190408</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2852 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        TripType  VisitNumber  ... DepartmentDescription FinelineNumber\n",
              "739          999          484  ...                   NaN            NaN\n",
              "740          999          484  ...                   NaN            NaN\n",
              "900            5          585  ...           PHARMACY RX            NaN\n",
              "944            5          619  ...           PHARMACY RX            NaN\n",
              "945            5          619  ...           PHARMACY RX            NaN\n",
              "...          ...          ...  ...                   ...            ...\n",
              "449840        40       190133  ...                   NaN            NaN\n",
              "450036       999       190182  ...                   NaN            NaN\n",
              "450163        40       190230  ...                   NaN            NaN\n",
              "450361        39       190309  ...                   NaN            NaN\n",
              "450595        41       190408  ...                   NaN            NaN\n",
              "\n",
              "[2852 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hkxghXc-bw_v"
      },
      "source": [
        "If `Upc` is `NaN`, then is `NaN` `FileNumber`?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5U3SxO8U-o4n",
        "outputId": "35b7fa43-2ae2-4edc-8eee-2a65b579a329",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "(original_df.Upc.isna().sum(),\n",
        " original_df.FinelineNumber.isna().sum(),\n",
        " (original_df.FinelineNumber.isna() & original_df.Upc.isna()).sum())"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2852, 2852, 2852)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TEuX-r_3-o4n"
      },
      "source": [
        "But it may be the case that both `Upc` and `FineLineNumber` are `NaN` but not the `DepartmentDescription` ...\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OeSCgx9pEqu2",
        "outputId": "723512ee-4d4d-4b8a-8013-b5ac1f32e81d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "fil = (original_df.FinelineNumber.isna() & original_df.Upc.isna())\n",
        "original_df[fil]['DepartmentDescription'].value_counts(dropna=False)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PHARMACY RX    1880\n",
              "NaN             972\n",
              "Name: DepartmentDescription, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zeMMoLD8dggY"
      },
      "source": [
        "Despite of previous case, where `Upc` and `FineLineNumber` are `NaN`\n",
        "\n",
        "`DepartmentDescription` has a value --> `PHARMACY RX`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hFkHycMWzUlW",
        "outputId": "ee305dc1-de80-4bdb-c785-ec71365ba906",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        }
      },
      "source": [
        "print(original_df[original_df.Upc.isna()].TripType.nunique())\n",
        "\n",
        "plt.figure(figsize=(16,8))\n",
        "sns.countplot(\n",
        "   original_df[original_df.Upc.isna()].TripType, color='dodgerblue')\n",
        "plt.title('Cantidad de UPC NaN por TripType')\n",
        "plt.xlabel('TripType')\n",
        "# plt.ylabel('Cant. de Mediciones')\n",
        "plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "36\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9rqPKb2g-o4p"
      },
      "source": [
        "So, `Upc` and `FinelineNumber` are both `NaN` at the same\n",
        "time."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gRJUObm2fmI9"
      },
      "source": [
        "### 1.2 Analysis\n",
        "\n",
        "Our last step in this analysis is to see how balanced is the data..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ByLMMzbL-o4s",
        "outputId": "2ca426ab-485f-4aba-9e1e-c4ecb45112bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        }
      },
      "source": [
        "print(original_df[['TripType']].nunique())\n",
        "\n",
        "plt.figure(figsize=(16,8))\n",
        "sns.countplot(\n",
        "    original_df.TripType, color='dodgerblue')\n",
        "plt.title('Cantidad de entradas por TripType')\n",
        "plt.xlabel('TripType')\n",
        "# plt.ylabel('Cant. de Mediciones')\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TripType    38\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sdpS1vBTzUle",
        "outputId": "2eccaba8-da5e-4f03-caeb-aa9c5874ef26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        }
      },
      "source": [
        "plt.figure(figsize=(16,8))\n",
        "sns.countplot(\n",
        "   original_df[original_df.ScanCount < 0].TripType, color='dodgerblue')\n",
        "plt.title('')\n",
        "plt.xlabel('TripType')\n",
        "plt.title('Cantidad de devoluciones por TripType')\n",
        "# plt.ylabel('Cant. de Mediciones')\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DRqWKtvxzUlg"
      },
      "source": [
        "del original_df"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5HtUZY6dgm55"
      },
      "source": [
        "## 2 Data Cleaning.\n",
        "---\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "deFObYgzNa0g"
      },
      "source": [
        "clean_df = pd.read_csv(file_path_train, dtype=dtype)"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wVcKUXDszUll"
      },
      "source": [
        "### 2.1 Get Labels `TripType`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4eZwEbGezUlm",
        "outputId": "c1e60b9e-891d-4e2f-ad89-0ce4583bce28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "y = clean_df.groupby(\n",
        "    ['VisitNumber', 'Weekday'], as_index=False).first().TripType\n",
        "y"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        999\n",
              "1          8\n",
              "2          8\n",
              "3         35\n",
              "4         41\n",
              "        ... \n",
              "67024     24\n",
              "67025     38\n",
              "67026     25\n",
              "67027     22\n",
              "67028      8\n",
              "Name: TripType, Length: 67029, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sKUqdzjKzUlp"
      },
      "source": [
        "### 2.2 Concat Test and Train Dataframes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AsLaRPdezUlp"
      },
      "source": [
        "test_df = pd.read_csv(file_path_test, dtype=dtype)"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XJhQ3la2zUlr",
        "outputId": "98afd555-c045-406b-cea5-9ce73cbdfbae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "clean_df = clean_df.drop(['TripType'], axis=1)\n",
        "clean_df"
      ],
      "execution_count": 44,
      "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>VisitNumber</th>\n",
              "      <th>Weekday</th>\n",
              "      <th>Upc</th>\n",
              "      <th>ScanCount</th>\n",
              "      <th>DepartmentDescription</th>\n",
              "      <th>FinelineNumber</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5</td>\n",
              "      <td>Friday</td>\n",
              "      <td>68113152929.0</td>\n",
              "      <td>-1</td>\n",
              "      <td>FINANCIAL SERVICES</td>\n",
              "      <td>1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9</td>\n",
              "      <td>Friday</td>\n",
              "      <td>1070080727.0</td>\n",
              "      <td>1</td>\n",
              "      <td>IMPULSE MERCHANDISE</td>\n",
              "      <td>115.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9</td>\n",
              "      <td>Friday</td>\n",
              "      <td>3107.0</td>\n",
              "      <td>1</td>\n",
              "      <td>PRODUCE</td>\n",
              "      <td>103.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9</td>\n",
              "      <td>Friday</td>\n",
              "      <td>4011.0</td>\n",
              "      <td>1</td>\n",
              "      <td>PRODUCE</td>\n",
              "      <td>5501.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10</td>\n",
              "      <td>Friday</td>\n",
              "      <td>6414410235.0</td>\n",
              "      <td>1</td>\n",
              "      <td>DSD GROCERY</td>\n",
              "      <td>2008.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>453406</th>\n",
              "      <td>191344</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>73150956660.0</td>\n",
              "      <td>1</td>\n",
              "      <td>BEAUTY</td>\n",
              "      <td>3405.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>453407</th>\n",
              "      <td>191344</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>65053002603.0</td>\n",
              "      <td>1</td>\n",
              "      <td>WIRELESS</td>\n",
              "      <td>1712.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>453408</th>\n",
              "      <td>191344</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>7918131034.0</td>\n",
              "      <td>1</td>\n",
              "      <td>BEAUTY</td>\n",
              "      <td>3405.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>453409</th>\n",
              "      <td>191347</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>4190007664.0</td>\n",
              "      <td>1</td>\n",
              "      <td>DAIRY</td>\n",
              "      <td>1512.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>453410</th>\n",
              "      <td>191347</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>3800059655.0</td>\n",
              "      <td>1</td>\n",
              "      <td>GROCERY DRY GOODS</td>\n",
              "      <td>3600.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>453411 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        VisitNumber Weekday  ... DepartmentDescription  FinelineNumber\n",
              "0                 5  Friday  ...    FINANCIAL SERVICES          1000.0\n",
              "1                 9  Friday  ...   IMPULSE MERCHANDISE           115.0\n",
              "2                 9  Friday  ...               PRODUCE           103.0\n",
              "3                 9  Friday  ...               PRODUCE          5501.0\n",
              "4                10  Friday  ...           DSD GROCERY          2008.0\n",
              "...             ...     ...  ...                   ...             ...\n",
              "453406       191344  Sunday  ...                BEAUTY          3405.0\n",
              "453407       191344  Sunday  ...              WIRELESS          1712.0\n",
              "453408       191344  Sunday  ...                BEAUTY          3405.0\n",
              "453409       191347  Sunday  ...                 DAIRY          1512.0\n",
              "453410       191347  Sunday  ...     GROCERY DRY GOODS          3600.0\n",
              "\n",
              "[453411 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tlkyVUtK2TYx"
      },
      "source": [
        "Set a flag for Training Set and Test Set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WM2uGZMOzUlu"
      },
      "source": [
        "clean_df['is_train_set'] = 1\n",
        "test_df['is_train_set'] = 0"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Tllha95zUlv"
      },
      "source": [
        "clean_df = pd.concat([clean_df, test_df])"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_X_FEYZw2fMA"
      },
      "source": [
        "Delete old test dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FaTRy22GzUlx"
      },
      "source": [
        "del test_df"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bg_-8V3LzUlz"
      },
      "source": [
        "### 2.3 `nan` values ?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XVoZ2_YQzUl0",
        "outputId": "5fa92b71-e607-4ddd-b444-07a313e5f39c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155
        }
      },
      "source": [
        "clean_df.isna().sum()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VisitNumber                 0\n",
              "Weekday                     0\n",
              "Upc                      4129\n",
              "ScanCount                   0\n",
              "DepartmentDescription    1361\n",
              "FinelineNumber           4129\n",
              "is_train_set                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9rEnfyELzUl2"
      },
      "source": [
        "### 2.4 return column\n",
        "\n",
        "New `return` and `ScanCount` columns from `ScanCount`\n",
        "\n",
        "`return`\n",
        "\n",
        "* `1` a return\n",
        "* `0` no return"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JxyRu4Sa24LF"
      },
      "source": [
        "def repay_column(df: pd.DataFrame):\n",
        "    \"\"\"\n",
        "    add new return column \n",
        "    \"\"\"\n",
        "    df['returns'] = df.apply(\n",
        "        lambda x: abs(x['ScanCount']) if x['ScanCount'] < 0 else 0, axis=1\n",
        "    )\n",
        "    return df"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qo9jtTay29I1",
        "outputId": "c63e731f-16b6-425c-9309-76316d790b9b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "clean_df = repay_column(clean_df)\n",
        "clean_df[['ScanCount', 'returns']]"
      ],
      "execution_count": 50,
      "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>ScanCount</th>\n",
              "      <th>returns</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193638</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193639</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193640</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193641</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193642</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>647054 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        ScanCount  returns\n",
              "0              -1        1\n",
              "1               1        0\n",
              "2               1        0\n",
              "3               1        0\n",
              "4               1        0\n",
              "...           ...      ...\n",
              "193638          1        0\n",
              "193639          1        0\n",
              "193640          1        0\n",
              "193641          1        0\n",
              "193642          1        0\n",
              "\n",
              "[647054 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "05rCbgHEzUl3"
      },
      "source": [
        "#### Positive ScanCount column\n",
        "\n",
        "Positive `ScanCount`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mvwQjAVM3QsE",
        "outputId": "754992cc-c102-4046-a7bf-69056cb567ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "clean_df.loc[clean_df.ScanCount < 0, 'ScanCount'] = 0\n",
        "clean_df"
      ],
      "execution_count": 51,
      "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>VisitNumber</th>\n",
              "      <th>Weekday</th>\n",
              "      <th>Upc</th>\n",
              "      <th>ScanCount</th>\n",
              "      <th>DepartmentDescription</th>\n",
              "      <th>FinelineNumber</th>\n",
              "      <th>is_train_set</th>\n",
              "      <th>returns</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5</td>\n",
              "      <td>Friday</td>\n",
              "      <td>68113152929.0</td>\n",
              "      <td>0</td>\n",
              "      <td>FINANCIAL SERVICES</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9</td>\n",
              "      <td>Friday</td>\n",
              "      <td>1070080727.0</td>\n",
              "      <td>1</td>\n",
              "      <td>IMPULSE MERCHANDISE</td>\n",
              "      <td>115.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9</td>\n",
              "      <td>Friday</td>\n",
              "      <td>3107.0</td>\n",
              "      <td>1</td>\n",
              "      <td>PRODUCE</td>\n",
              "      <td>103.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9</td>\n",
              "      <td>Friday</td>\n",
              "      <td>4011.0</td>\n",
              "      <td>1</td>\n",
              "      <td>PRODUCE</td>\n",
              "      <td>5501.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10</td>\n",
              "      <td>Friday</td>\n",
              "      <td>6414410235.0</td>\n",
              "      <td>1</td>\n",
              "      <td>DSD GROCERY</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193638</th>\n",
              "      <td>191346</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>3120033013.0</td>\n",
              "      <td>1</td>\n",
              "      <td>DSD GROCERY</td>\n",
              "      <td>4639.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193639</th>\n",
              "      <td>191346</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>3700091229.0</td>\n",
              "      <td>1</td>\n",
              "      <td>HOUSEHOLD CHEMICALS/SUPP</td>\n",
              "      <td>8947.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193640</th>\n",
              "      <td>191346</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>32390001778.0</td>\n",
              "      <td>1</td>\n",
              "      <td>PHARMACY OTC</td>\n",
              "      <td>1118.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193641</th>\n",
              "      <td>191346</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>7874205336.0</td>\n",
              "      <td>1</td>\n",
              "      <td>FROZEN FOODS</td>\n",
              "      <td>1752.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193642</th>\n",
              "      <td>191346</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>4072.0</td>\n",
              "      <td>1</td>\n",
              "      <td>PRODUCE</td>\n",
              "      <td>4170.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>647054 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        VisitNumber Weekday  ... is_train_set  returns\n",
              "0                 5  Friday  ...            1        1\n",
              "1                 9  Friday  ...            1        0\n",
              "2                 9  Friday  ...            1        0\n",
              "3                 9  Friday  ...            1        0\n",
              "4                10  Friday  ...            1        0\n",
              "...             ...     ...  ...          ...      ...\n",
              "193638       191346  Sunday  ...            0        0\n",
              "193639       191346  Sunday  ...            0        0\n",
              "193640       191346  Sunday  ...            0        0\n",
              "193641       191346  Sunday  ...            0        0\n",
              "193642       191346  Sunday  ...            0        0\n",
              "\n",
              "[647054 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6iQEVSHwzUl5"
      },
      "source": [
        "### 2.5 `UPC` columns\n",
        "\n",
        "In its standard version (UPC-A), the bar code consists of a five digit \n",
        "manufacturer number and a five digit product number.  In addition there is a\n",
        "1 digit number system identifier at the start of the code. The number \n",
        "system digit denotes the use of one of ten number systems defined by UPC:\n",
        "\n",
        "* `0, 1 , 6, 7 and 8` are for regular UPC codes.\n",
        "* `2` is for random weight items, e.g. meat, marked in-store.\n",
        "* `3` is for National Drug Code and National Health Related Items.\n",
        "* `4` is for in-store marking of non-food items.\n",
        "* `5 and 9` are for coupon use.\n",
        "\n",
        "<p style=\"text-align: center;\">\n",
        "<img src=http://www.computalabel.com/Images/UPCdiag.png width=75%>\n",
        "</p>\n",
        "\n",
        "\n",
        "\n",
        "The UPC symbol also has a `check digit` which is the last digit of the \n",
        "code and is calculated according to the algorithm used for EAN.\n",
        "\n",
        "First step add `0's` to some values in Upc column to clomplete 11 digits\n",
        "\n",
        "if values is `nan` = `'nan'` (str)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i23DJ2NE0iiS"
      },
      "source": [
        "def clean_upc(df):\n",
        "    \n",
        "    def f(x):\n",
        "        if x == '-1' or not isinstance(x, str) :\n",
        "            x = '-1'\n",
        "        elif len(x) < 11:\n",
        "            x = '0' * (11 - len(x)) + x\n",
        "        return x\n",
        "    \n",
        "    df.Upc = df.Upc.str[:-2].apply(f)\n",
        "    return df"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dQl2g6CLzUl5",
        "outputId": "4b4b4228-c068-40a3-c6b2-751d47b77691",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "clean_df = clean_upc(clean_df)\n",
        "clean_df[['Upc']]"
      ],
      "execution_count": 53,
      "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>Upc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>68113152929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>01070080727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>00000003107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>00000004011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>06414410235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193638</th>\n",
              "      <td>03120033013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193639</th>\n",
              "      <td>03700091229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193640</th>\n",
              "      <td>32390001778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193641</th>\n",
              "      <td>07874205336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193642</th>\n",
              "      <td>00000004072</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>647054 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                Upc\n",
              "0       68113152929\n",
              "1       01070080727\n",
              "2       00000003107\n",
              "3       00000004011\n",
              "4       06414410235\n",
              "...             ...\n",
              "193638  03120033013\n",
              "193639  03700091229\n",
              "193640  32390001778\n",
              "193641  07874205336\n",
              "193642  00000004072\n",
              "\n",
              "[647054 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CS7zdQrkzUl7"
      },
      "source": [
        "#### numSysChar Column\n",
        "\n",
        "* `0, 1 , 6, 7 and 8` are for regular UPC codes.\n",
        "* `2` is for random weight items, e.g. meat, marked in-store.\n",
        "* `3` is for National Drug Code and National Health Related Items.\n",
        "* `4` is for in-store marking of non-food items.\n",
        "* `5 and 9` are for coupon use."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Atm2ldLj30Jq"
      },
      "source": [
        "def upc_columns(df):\n",
        "    df['numSysChar'] = df.apply(\n",
        "        lambda x: x.Upc[0] if x.Upc != '-1' else '-1', axis=1)\n",
        "    df['manNum'] = df.apply(\n",
        "        lambda x: x.Upc[1:6] if x.Upc != '-1' else '-1', axis=1)\n",
        "    #df['itemNum'] = df.apply(\n",
        "    #     lambda x: x.Upc[6:11] if x.Upc != '-1' else '-1', axis=1)\n",
        "    \n",
        "    # df['checkDig'] = df.apply(\n",
        "    #     lambda x: int(x.Upc[-1]) if isinstance(x.Upc, str) else -1, axis=1)\n",
        "    return df"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XqTYHRcs4YzH",
        "outputId": "d65f13e3-4543-4eb8-a6a4-19db435badd3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "clean_df = upc_columns(clean_df)\n",
        "clean_df[['Upc', 'numSysChar', 'manNum']]"
      ],
      "execution_count": 56,
      "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>Upc</th>\n",
              "      <th>numSysChar</th>\n",
              "      <th>manNum</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>68113152929</td>\n",
              "      <td>6</td>\n",
              "      <td>81131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>01070080727</td>\n",
              "      <td>0</td>\n",
              "      <td>10700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>00000003107</td>\n",
              "      <td>0</td>\n",
              "      <td>00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>00000004011</td>\n",
              "      <td>0</td>\n",
              "      <td>00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>06414410235</td>\n",
              "      <td>0</td>\n",
              "      <td>64144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193638</th>\n",
              "      <td>03120033013</td>\n",
              "      <td>0</td>\n",
              "      <td>31200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193639</th>\n",
              "      <td>03700091229</td>\n",
              "      <td>0</td>\n",
              "      <td>37000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193640</th>\n",
              "      <td>32390001778</td>\n",
              "      <td>3</td>\n",
              "      <td>23900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193641</th>\n",
              "      <td>07874205336</td>\n",
              "      <td>0</td>\n",
              "      <td>78742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193642</th>\n",
              "      <td>00000004072</td>\n",
              "      <td>0</td>\n",
              "      <td>00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>647054 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                Upc numSysChar manNum\n",
              "0       68113152929          6  81131\n",
              "1       01070080727          0  10700\n",
              "2       00000003107          0  00000\n",
              "3       00000004011          0  00000\n",
              "4       06414410235          0  64144\n",
              "...             ...        ...    ...\n",
              "193638  03120033013          0  31200\n",
              "193639  03700091229          0  37000\n",
              "193640  32390001778          3  23900\n",
              "193641  07874205336          0  78742\n",
              "193642  00000004072          0  00000\n",
              "\n",
              "[647054 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7OY5tIJB4o18"
      },
      "source": [
        "fil = ['0','1','6','7', '8']\n",
        "clean_df.loc[clean_df.numSysChar.isin(fil), 'numSysChar'] = 'regular'\n",
        "fil = ['5','9']\n",
        "clean_df.loc[clean_df.numSysChar.isin(fil), 'numSysChar'] = 'cupon'"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IfOiQX5Q4soe",
        "outputId": "1ee45964-3d77-4591-a341-620693765245",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "source": [
        "clean_df.numSysChar.value_counts()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "regular    612971\n",
              "3           13892\n",
              "2           13706\n",
              "-1           4129\n",
              "4            1762\n",
              "cupon         594\n",
              "Name: numSysChar, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Nh58A0bzUl8"
      },
      "source": [
        "### 2.6 drop columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TJyN8Q4MzUl9"
      },
      "source": [
        "clean_df.Upc = clean_df.Upc.astype('float')\n",
        "clean_df.FinelineNumber = clean_df.FinelineNumber.astype('float')\n",
        "clean_df.manNum = clean_df.manNum.astype('int')"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "neRKrcs2zUl-",
        "outputId": "e3359e93-ada3-4aff-af93-5c8e03532cfc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        }
      },
      "source": [
        "clean_df"
      ],
      "execution_count": 60,
      "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>VisitNumber</th>\n",
              "      <th>Weekday</th>\n",
              "      <th>Upc</th>\n",
              "      <th>ScanCount</th>\n",
              "      <th>DepartmentDescription</th>\n",
              "      <th>FinelineNumber</th>\n",
              "      <th>is_train_set</th>\n",
              "      <th>returns</th>\n",
              "      <th>numSysChar</th>\n",
              "      <th>manNum</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5</td>\n",
              "      <td>Friday</td>\n",
              "      <td>6.811315e+10</td>\n",
              "      <td>0</td>\n",
              "      <td>FINANCIAL SERVICES</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>regular</td>\n",
              "      <td>81131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9</td>\n",
              "      <td>Friday</td>\n",
              "      <td>1.070081e+09</td>\n",
              "      <td>1</td>\n",
              "      <td>IMPULSE MERCHANDISE</td>\n",
              "      <td>115.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>regular</td>\n",
              "      <td>10700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9</td>\n",
              "      <td>Friday</td>\n",
              "      <td>3.107000e+03</td>\n",
              "      <td>1</td>\n",
              "      <td>PRODUCE</td>\n",
              "      <td>103.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>regular</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9</td>\n",
              "      <td>Friday</td>\n",
              "      <td>4.011000e+03</td>\n",
              "      <td>1</td>\n",
              "      <td>PRODUCE</td>\n",
              "      <td>5501.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>regular</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10</td>\n",
              "      <td>Friday</td>\n",
              "      <td>6.414410e+09</td>\n",
              "      <td>1</td>\n",
              "      <td>DSD GROCERY</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>regular</td>\n",
              "      <td>64144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193638</th>\n",
              "      <td>191346</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>3.120033e+09</td>\n",
              "      <td>1</td>\n",
              "      <td>DSD GROCERY</td>\n",
              "      <td>4639.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>regular</td>\n",
              "      <td>31200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193639</th>\n",
              "      <td>191346</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>3.700091e+09</td>\n",
              "      <td>1</td>\n",
              "      <td>HOUSEHOLD CHEMICALS/SUPP</td>\n",
              "      <td>8947.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>regular</td>\n",
              "      <td>37000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193640</th>\n",
              "      <td>191346</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>3.239000e+10</td>\n",
              "      <td>1</td>\n",
              "      <td>PHARMACY OTC</td>\n",
              "      <td>1118.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>23900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193641</th>\n",
              "      <td>191346</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>7.874205e+09</td>\n",
              "      <td>1</td>\n",
              "      <td>FROZEN FOODS</td>\n",
              "      <td>1752.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>regular</td>\n",
              "      <td>78742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193642</th>\n",
              "      <td>191346</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>4.072000e+03</td>\n",
              "      <td>1</td>\n",
              "      <td>PRODUCE</td>\n",
              "      <td>4170.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>regular</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>647054 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        VisitNumber Weekday           Upc  ...  returns numSysChar  manNum\n",
              "0                 5  Friday  6.811315e+10  ...        1    regular   81131\n",
              "1                 9  Friday  1.070081e+09  ...        0    regular   10700\n",
              "2                 9  Friday  3.107000e+03  ...        0    regular       0\n",
              "3                 9  Friday  4.011000e+03  ...        0    regular       0\n",
              "4                10  Friday  6.414410e+09  ...        0    regular   64144\n",
              "...             ...     ...           ...  ...      ...        ...     ...\n",
              "193638       191346  Sunday  3.120033e+09  ...        0    regular   31200\n",
              "193639       191346  Sunday  3.700091e+09  ...        0    regular   37000\n",
              "193640       191346  Sunday  3.239000e+10  ...        0          3   23900\n",
              "193641       191346  Sunday  7.874205e+09  ...        0    regular   78742\n",
              "193642       191346  Sunday  4.072000e+03  ...        0    regular       0\n",
              "\n",
              "[647054 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rhJdM5YDzUmA"
      },
      "source": [
        "### 2.7 Dummies, groupby columns \n",
        "\n",
        "Now, we create the dummy columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qGBQ9f8TzUmB",
        "outputId": "415b4c3c-ac24-4415-99d3-7c6d811fd8c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        }
      },
      "source": [
        "clean_df = pd.get_dummies(\n",
        "    clean_df, \n",
        "    columns=['DepartmentDescription'], \n",
        "    dummy_na=True)\n",
        "clean_df"
      ],
      "execution_count": 61,
      "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>VisitNumber</th>\n",
              "      <th>Weekday</th>\n",
              "      <th>Upc</th>\n",
              "      <th>ScanCount</th>\n",
              "      <th>FinelineNumber</th>\n",
              "      <th>is_train_set</th>\n",
              "      <th>returns</th>\n",
              "      <th>numSysChar</th>\n",
              "      <th>manNum</th>\n",
              "      <th>DepartmentDescription_1-HR PHOTO</th>\n",
              "      <th>DepartmentDescription_ACCESSORIES</th>\n",
              "      <th>DepartmentDescription_AUTOMOTIVE</th>\n",
              "      <th>DepartmentDescription_BAKERY</th>\n",
              "      <th>DepartmentDescription_BATH AND SHOWER</th>\n",
              "      <th>DepartmentDescription_BEAUTY</th>\n",
              "      <th>DepartmentDescription_BEDDING</th>\n",
              "      <th>DepartmentDescription_BOOKS AND MAGAZINES</th>\n",
              "      <th>DepartmentDescription_BOYS WEAR</th>\n",
              "      <th>DepartmentDescription_BRAS &amp; SHAPEWEAR</th>\n",
              "      <th>DepartmentDescription_CAMERAS AND SUPPLIES</th>\n",
              "      <th>DepartmentDescription_CANDY, TOBACCO, COOKIES</th>\n",
              "      <th>DepartmentDescription_CELEBRATION</th>\n",
              "      <th>DepartmentDescription_COMM BREAD</th>\n",
              "      <th>DepartmentDescription_CONCEPT STORES</th>\n",
              "      <th>DepartmentDescription_COOK AND DINE</th>\n",
              "      <th>DepartmentDescription_DAIRY</th>\n",
              "      <th>DepartmentDescription_DSD GROCERY</th>\n",
              "      <th>DepartmentDescription_ELECTRONICS</th>\n",
              "      <th>DepartmentDescription_FABRICS AND CRAFTS</th>\n",
              "      <th>DepartmentDescription_FINANCIAL SERVICES</th>\n",
              "      <th>DepartmentDescription_FROZEN FOODS</th>\n",
              "      <th>DepartmentDescription_FURNITURE</th>\n",
              "      <th>DepartmentDescription_GIRLS WEAR, 4-6X  AND 7-14</th>\n",
              "      <th>DepartmentDescription_GROCERY DRY GOODS</th>\n",
              "      <th>DepartmentDescription_HARDWARE</th>\n",
              "      <th>DepartmentDescription_HEALTH AND BEAUTY AIDS</th>\n",
              "      <th>DepartmentDescription_HOME DECOR</th>\n",
              "      <th>DepartmentDescription_HOME MANAGEMENT</th>\n",
              "      <th>DepartmentDescription_HORTICULTURE AND ACCESS</th>\n",
              "      <th>DepartmentDescription_HOUSEHOLD CHEMICALS/SUPP</th>\n",
              "      <th>DepartmentDescription_HOUSEHOLD PAPER GOODS</th>\n",
              "      <th>DepartmentDescription_IMPULSE MERCHANDISE</th>\n",
              "      <th>DepartmentDescription_INFANT APPAREL</th>\n",
              "      <th>DepartmentDescription_INFANT CONSUMABLE HARDLINES</th>\n",
              "      <th>DepartmentDescription_JEWELRY AND SUNGLASSES</th>\n",
              "      <th>DepartmentDescription_LADIES SOCKS</th>\n",
              "      <th>DepartmentDescription_LADIESWEAR</th>\n",
              "      <th>DepartmentDescription_LARGE HOUSEHOLD GOODS</th>\n",
              "      <th>DepartmentDescription_LAWN AND GARDEN</th>\n",
              "      <th>DepartmentDescription_LIQUOR,WINE,BEER</th>\n",
              "      <th>DepartmentDescription_MEAT - FRESH &amp; FROZEN</th>\n",
              "      <th>DepartmentDescription_MEDIA AND GAMING</th>\n",
              "      <th>DepartmentDescription_MENS WEAR</th>\n",
              "      <th>DepartmentDescription_MENSWEAR</th>\n",
              "      <th>DepartmentDescription_OFFICE SUPPLIES</th>\n",
              "      <th>DepartmentDescription_OPTICAL - FRAMES</th>\n",
              "      <th>DepartmentDescription_OPTICAL - LENSES</th>\n",
              "      <th>DepartmentDescription_OTHER DEPARTMENTS</th>\n",
              "      <th>DepartmentDescription_PAINT AND ACCESSORIES</th>\n",
              "      <th>DepartmentDescription_PERSONAL CARE</th>\n",
              "      <th>DepartmentDescription_PETS AND SUPPLIES</th>\n",
              "      <th>DepartmentDescription_PHARMACY OTC</th>\n",
              "      <th>DepartmentDescription_PHARMACY RX</th>\n",
              "      <th>DepartmentDescription_PLAYERS AND ELECTRONICS</th>\n",
              "      <th>DepartmentDescription_PLUS AND MATERNITY</th>\n",
              "      <th>DepartmentDescription_PRE PACKED DELI</th>\n",
              "      <th>DepartmentDescription_PRODUCE</th>\n",
              "      <th>DepartmentDescription_SEAFOOD</th>\n",
              "      <th>DepartmentDescription_SEASONAL</th>\n",
              "      <th>DepartmentDescription_SERVICE DELI</th>\n",
              "      <th>DepartmentDescription_SHEER HOSIERY</th>\n",
              "      <th>DepartmentDescription_SHOES</th>\n",
              "      <th>DepartmentDescription_SLEEPWEAR/FOUNDATIONS</th>\n",
              "      <th>DepartmentDescription_SPORTING GOODS</th>\n",
              "      <th>DepartmentDescription_SWIMWEAR/OUTERWEAR</th>\n",
              "      <th>DepartmentDescription_TOYS</th>\n",
              "      <th>DepartmentDescription_WIRELESS</th>\n",
              "      <th>DepartmentDescription_nan</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5</td>\n",
              "      <td>Friday</td>\n",
              "      <td>6.811315e+10</td>\n",
              "      <td>0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>regular</td>\n",
              "      <td>81131</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9</td>\n",
              "      <td>Friday</td>\n",
              "      <td>1.070081e+09</td>\n",
              "      <td>1</td>\n",
              "      <td>115.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>regular</td>\n",
              "      <td>10700</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9</td>\n",
              "      <td>Friday</td>\n",
              "      <td>3.107000e+03</td>\n",
              "      <td>1</td>\n",
              "      <td>103.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>regular</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9</td>\n",
              "      <td>Friday</td>\n",
              "      <td>4.011000e+03</td>\n",
              "      <td>1</td>\n",
              "      <td>5501.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>regular</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10</td>\n",
              "      <td>Friday</td>\n",
              "      <td>6.414410e+09</td>\n",
              "      <td>1</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>regular</td>\n",
              "      <td>64144</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193638</th>\n",
              "      <td>191346</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>3.120033e+09</td>\n",
              "      <td>1</td>\n",
              "      <td>4639.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>regular</td>\n",
              "      <td>31200</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193639</th>\n",
              "      <td>191346</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>3.700091e+09</td>\n",
              "      <td>1</td>\n",
              "      <td>8947.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>regular</td>\n",
              "      <td>37000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193640</th>\n",
              "      <td>191346</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>3.239000e+10</td>\n",
              "      <td>1</td>\n",
              "      <td>1118.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>23900</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193641</th>\n",
              "      <td>191346</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>7.874205e+09</td>\n",
              "      <td>1</td>\n",
              "      <td>1752.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>regular</td>\n",
              "      <td>78742</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193642</th>\n",
              "      <td>191346</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>4.072000e+03</td>\n",
              "      <td>1</td>\n",
              "      <td>4170.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>regular</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>647054 rows × 78 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        VisitNumber  ... DepartmentDescription_nan\n",
              "0                 5  ...                         0\n",
              "1                 9  ...                         0\n",
              "2                 9  ...                         0\n",
              "3                 9  ...                         0\n",
              "4                10  ...                         0\n",
              "...             ...  ...                       ...\n",
              "193638       191346  ...                         0\n",
              "193639       191346  ...                         0\n",
              "193640       191346  ...                         0\n",
              "193641       191346  ...                         0\n",
              "193642       191346  ...                         0\n",
              "\n",
              "[647054 rows x 78 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U_45Tnor57gM"
      },
      "source": [
        "#### `numSysChar` Dummies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W_Fpqhzf5nJa",
        "outputId": "2479ad06-0435-42cd-9eca-37d98d6f5b1c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        }
      },
      "source": [
        "clean_df = pd.get_dummies(\n",
        "    clean_df, \n",
        "    columns=['numSysChar'], \n",
        "    dummy_na=False)\n",
        "clean_df"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "        vertical-align: middle;\n",
              "    }\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>VisitNumber</th>\n",
              "      <th>Weekday</th>\n",
              "      <th>Upc</th>\n",
              "      <th>ScanCount</th>\n",
              "      <th>FinelineNumber</th>\n",
              "      <th>is_train_set</th>\n",
              "      <th>returns</th>\n",
              "      <th>manNum</th>\n",
              "      <th>DepartmentDescription_1-HR PHOTO</th>\n",
              "      <th>DepartmentDescription_ACCESSORIES</th>\n",
              "      <th>DepartmentDescription_AUTOMOTIVE</th>\n",
              "      <th>DepartmentDescription_BAKERY</th>\n",
              "      <th>DepartmentDescription_BATH AND SHOWER</th>\n",
              "      <th>DepartmentDescription_BEAUTY</th>\n",
              "      <th>DepartmentDescription_BEDDING</th>\n",
              "      <th>DepartmentDescription_BOOKS AND MAGAZINES</th>\n",
              "      <th>DepartmentDescription_BOYS WEAR</th>\n",
              "      <th>DepartmentDescription_BRAS &amp; SHAPEWEAR</th>\n",
              "      <th>DepartmentDescription_CAMERAS AND SUPPLIES</th>\n",
              "      <th>DepartmentDescription_CANDY, TOBACCO, COOKIES</th>\n",
              "      <th>DepartmentDescription_CELEBRATION</th>\n",
              "      <th>DepartmentDescription_COMM BREAD</th>\n",
              "      <th>DepartmentDescription_CONCEPT STORES</th>\n",
              "      <th>DepartmentDescription_COOK AND DINE</th>\n",
              "      <th>DepartmentDescription_DAIRY</th>\n",
              "      <th>DepartmentDescription_DSD GROCERY</th>\n",
              "      <th>DepartmentDescription_ELECTRONICS</th>\n",
              "      <th>DepartmentDescription_FABRICS AND CRAFTS</th>\n",
              "      <th>DepartmentDescription_FINANCIAL SERVICES</th>\n",
              "      <th>DepartmentDescription_FROZEN FOODS</th>\n",
              "      <th>DepartmentDescription_FURNITURE</th>\n",
              "      <th>DepartmentDescription_GIRLS WEAR, 4-6X  AND 7-14</th>\n",
              "      <th>DepartmentDescription_GROCERY DRY GOODS</th>\n",
              "      <th>DepartmentDescription_HARDWARE</th>\n",
              "      <th>DepartmentDescription_HEALTH AND BEAUTY AIDS</th>\n",
              "      <th>DepartmentDescription_HOME DECOR</th>\n",
              "      <th>DepartmentDescription_HOME MANAGEMENT</th>\n",
              "      <th>DepartmentDescription_HORTICULTURE AND ACCESS</th>\n",
              "      <th>DepartmentDescription_HOUSEHOLD CHEMICALS/SUPP</th>\n",
              "      <th>DepartmentDescription_HOUSEHOLD PAPER GOODS</th>\n",
              "      <th>...</th>\n",
              "      <th>DepartmentDescription_JEWELRY AND SUNGLASSES</th>\n",
              "      <th>DepartmentDescription_LADIES SOCKS</th>\n",
              "      <th>DepartmentDescription_LADIESWEAR</th>\n",
              "      <th>DepartmentDescription_LARGE HOUSEHOLD GOODS</th>\n",
              "      <th>DepartmentDescription_LAWN AND GARDEN</th>\n",
              "      <th>DepartmentDescription_LIQUOR,WINE,BEER</th>\n",
              "      <th>DepartmentDescription_MEAT - FRESH &amp; FROZEN</th>\n",
              "      <th>DepartmentDescription_MEDIA AND GAMING</th>\n",
              "      <th>DepartmentDescription_MENS WEAR</th>\n",
              "      <th>DepartmentDescription_MENSWEAR</th>\n",
              "      <th>DepartmentDescription_OFFICE SUPPLIES</th>\n",
              "      <th>DepartmentDescription_OPTICAL - FRAMES</th>\n",
              "      <th>DepartmentDescription_OPTICAL - LENSES</th>\n",
              "      <th>DepartmentDescription_OTHER DEPARTMENTS</th>\n",
              "      <th>DepartmentDescription_PAINT AND ACCESSORIES</th>\n",
              "      <th>DepartmentDescription_PERSONAL CARE</th>\n",
              "      <th>DepartmentDescription_PETS AND SUPPLIES</th>\n",
              "      <th>DepartmentDescription_PHARMACY OTC</th>\n",
              "      <th>DepartmentDescription_PHARMACY RX</th>\n",
              "      <th>DepartmentDescription_PLAYERS AND ELECTRONICS</th>\n",
              "      <th>DepartmentDescription_PLUS AND MATERNITY</th>\n",
              "      <th>DepartmentDescription_PRE PACKED DELI</th>\n",
              "      <th>DepartmentDescription_PRODUCE</th>\n",
              "      <th>DepartmentDescription_SEAFOOD</th>\n",
              "      <th>DepartmentDescription_SEASONAL</th>\n",
              "      <th>DepartmentDescription_SERVICE DELI</th>\n",
              "      <th>DepartmentDescription_SHEER HOSIERY</th>\n",
              "      <th>DepartmentDescription_SHOES</th>\n",
              "      <th>DepartmentDescription_SLEEPWEAR/FOUNDATIONS</th>\n",
              "      <th>DepartmentDescription_SPORTING GOODS</th>\n",
              "      <th>DepartmentDescription_SWIMWEAR/OUTERWEAR</th>\n",
              "      <th>DepartmentDescription_TOYS</th>\n",
              "      <th>DepartmentDescription_WIRELESS</th>\n",
              "      <th>DepartmentDescription_nan</th>\n",
              "      <th>numSysChar_-1</th>\n",
              "      <th>numSysChar_2</th>\n",
              "      <th>numSysChar_3</th>\n",
              "      <th>numSysChar_4</th>\n",
              "      <th>numSysChar_cupon</th>\n",
              "      <th>numSysChar_regular</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5</td>\n",
              "      <td>Friday</td>\n",
              "      <td>6.811315e+10</td>\n",
              "      <td>0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>81131</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9</td>\n",
              "      <td>Friday</td>\n",
              "      <td>1.070081e+09</td>\n",
              "      <td>1</td>\n",
              "      <td>115.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>10700</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9</td>\n",
              "      <td>Friday</td>\n",
              "      <td>3.107000e+03</td>\n",
              "      <td>1</td>\n",
              "      <td>103.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9</td>\n",
              "      <td>Friday</td>\n",
              "      <td>4.011000e+03</td>\n",
              "      <td>1</td>\n",
              "      <td>5501.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10</td>\n",
              "      <td>Friday</td>\n",
              "      <td>6.414410e+09</td>\n",
              "      <td>1</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>64144</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>193638</th>\n",
              "      <td>191346</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>3.120033e+09</td>\n",
              "      <td>1</td>\n",
              "      <td>4639.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>31200</td>\n",
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              "      <td>0</td>\n",
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              "      <td>...</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>193639</th>\n",
              "      <td>191346</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>3.700091e+09</td>\n",
              "      <td>1</td>\n",
              "      <td>8947.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>37000</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193640</th>\n",
              "      <td>191346</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>3.239000e+10</td>\n",
              "      <td>1</td>\n",
              "      <td>1118.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23900</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193641</th>\n",
              "      <td>191346</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>7.874205e+09</td>\n",
              "      <td>1</td>\n",
              "      <td>1752.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>78742</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193642</th>\n",
              "      <td>191346</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>4.072000e+03</td>\n",
              "      <td>1</td>\n",
              "      <td>4170.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>...</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>647054 rows × 83 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        VisitNumber Weekday  ...  numSysChar_cupon  numSysChar_regular\n",
              "0                 5  Friday  ...                 0                   1\n",
              "1                 9  Friday  ...                 0                   1\n",
              "2                 9  Friday  ...                 0                   1\n",
              "3                 9  Friday  ...                 0                   1\n",
              "4                10  Friday  ...                 0                   1\n",
              "...             ...     ...  ...               ...                 ...\n",
              "193638       191346  Sunday  ...                 0                   1\n",
              "193639       191346  Sunday  ...                 0                   1\n",
              "193640       191346  Sunday  ...                 0                   0\n",
              "193641       191346  Sunday  ...                 0                   1\n",
              "193642       191346  Sunday  ...                 0                   1\n",
              "\n",
              "[647054 rows x 83 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7FE7llzm-o44"
      },
      "source": [
        "#### group by VisitNumber and Weekday  \n",
        "Now, we group by the VisitNumber and Weekday (they should be the same), \n",
        "and add all values for ScanCount, and the one-hot encoding \n",
        "of `DepartmentDescriptioin`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aKQXdpBQzUmD"
      },
      "source": [
        "clean_df = clean_df.groupby(['VisitNumber', 'Weekday'], as_index=False).sum()"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iWp9w6IyzUmF",
        "outputId": "3a319329-604b-487a-8e9e-4cc09266cf19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        }
      },
      "source": [
        "clean_df"
      ],
      "execution_count": 64,
      "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>VisitNumber</th>\n",
              "      <th>Weekday</th>\n",
              "      <th>Upc</th>\n",
              "      <th>ScanCount</th>\n",
              "      <th>FinelineNumber</th>\n",
              "      <th>is_train_set</th>\n",
              "      <th>returns</th>\n",
              "      <th>manNum</th>\n",
              "      <th>DepartmentDescription_1-HR PHOTO</th>\n",
              "      <th>DepartmentDescription_ACCESSORIES</th>\n",
              "      <th>DepartmentDescription_AUTOMOTIVE</th>\n",
              "      <th>DepartmentDescription_BAKERY</th>\n",
              "      <th>DepartmentDescription_BATH AND SHOWER</th>\n",
              "      <th>DepartmentDescription_BEAUTY</th>\n",
              "      <th>DepartmentDescription_BEDDING</th>\n",
              "      <th>DepartmentDescription_BOOKS AND MAGAZINES</th>\n",
              "      <th>DepartmentDescription_BOYS WEAR</th>\n",
              "      <th>DepartmentDescription_BRAS &amp; SHAPEWEAR</th>\n",
              "      <th>DepartmentDescription_CAMERAS AND SUPPLIES</th>\n",
              "      <th>DepartmentDescription_CANDY, TOBACCO, COOKIES</th>\n",
              "      <th>DepartmentDescription_CELEBRATION</th>\n",
              "      <th>DepartmentDescription_COMM BREAD</th>\n",
              "      <th>DepartmentDescription_CONCEPT STORES</th>\n",
              "      <th>DepartmentDescription_COOK AND DINE</th>\n",
              "      <th>DepartmentDescription_DAIRY</th>\n",
              "      <th>DepartmentDescription_DSD GROCERY</th>\n",
              "      <th>DepartmentDescription_ELECTRONICS</th>\n",
              "      <th>DepartmentDescription_FABRICS AND CRAFTS</th>\n",
              "      <th>DepartmentDescription_FINANCIAL SERVICES</th>\n",
              "      <th>DepartmentDescription_FROZEN FOODS</th>\n",
              "      <th>DepartmentDescription_FURNITURE</th>\n",
              "      <th>DepartmentDescription_GIRLS WEAR, 4-6X  AND 7-14</th>\n",
              "      <th>DepartmentDescription_GROCERY DRY GOODS</th>\n",
              "      <th>DepartmentDescription_HARDWARE</th>\n",
              "      <th>DepartmentDescription_HEALTH AND BEAUTY AIDS</th>\n",
              "      <th>DepartmentDescription_HOME DECOR</th>\n",
              "      <th>DepartmentDescription_HOME MANAGEMENT</th>\n",
              "      <th>DepartmentDescription_HORTICULTURE AND ACCESS</th>\n",
              "      <th>DepartmentDescription_HOUSEHOLD CHEMICALS/SUPP</th>\n",
              "      <th>DepartmentDescription_HOUSEHOLD PAPER GOODS</th>\n",
              "      <th>...</th>\n",
              "      <th>DepartmentDescription_JEWELRY AND SUNGLASSES</th>\n",
              "      <th>DepartmentDescription_LADIES SOCKS</th>\n",
              "      <th>DepartmentDescription_LADIESWEAR</th>\n",
              "      <th>DepartmentDescription_LARGE HOUSEHOLD GOODS</th>\n",
              "      <th>DepartmentDescription_LAWN AND GARDEN</th>\n",
              "      <th>DepartmentDescription_LIQUOR,WINE,BEER</th>\n",
              "      <th>DepartmentDescription_MEAT - FRESH &amp; FROZEN</th>\n",
              "      <th>DepartmentDescription_MEDIA AND GAMING</th>\n",
              "      <th>DepartmentDescription_MENS WEAR</th>\n",
              "      <th>DepartmentDescription_MENSWEAR</th>\n",
              "      <th>DepartmentDescription_OFFICE SUPPLIES</th>\n",
              "      <th>DepartmentDescription_OPTICAL - FRAMES</th>\n",
              "      <th>DepartmentDescription_OPTICAL - LENSES</th>\n",
              "      <th>DepartmentDescription_OTHER DEPARTMENTS</th>\n",
              "      <th>DepartmentDescription_PAINT AND ACCESSORIES</th>\n",
              "      <th>DepartmentDescription_PERSONAL CARE</th>\n",
              "      <th>DepartmentDescription_PETS AND SUPPLIES</th>\n",
              "      <th>DepartmentDescription_PHARMACY OTC</th>\n",
              "      <th>DepartmentDescription_PHARMACY RX</th>\n",
              "      <th>DepartmentDescription_PLAYERS AND ELECTRONICS</th>\n",
              "      <th>DepartmentDescription_PLUS AND MATERNITY</th>\n",
              "      <th>DepartmentDescription_PRE PACKED DELI</th>\n",
              "      <th>DepartmentDescription_PRODUCE</th>\n",
              "      <th>DepartmentDescription_SEAFOOD</th>\n",
              "      <th>DepartmentDescription_SEASONAL</th>\n",
              "      <th>DepartmentDescription_SERVICE DELI</th>\n",
              "      <th>DepartmentDescription_SHEER HOSIERY</th>\n",
              "      <th>DepartmentDescription_SHOES</th>\n",
              "      <th>DepartmentDescription_SLEEPWEAR/FOUNDATIONS</th>\n",
              "      <th>DepartmentDescription_SPORTING GOODS</th>\n",
              "      <th>DepartmentDescription_SWIMWEAR/OUTERWEAR</th>\n",
              "      <th>DepartmentDescription_TOYS</th>\n",
              "      <th>DepartmentDescription_WIRELESS</th>\n",
              "      <th>DepartmentDescription_nan</th>\n",
              "      <th>numSysChar_-1</th>\n",
              "      <th>numSysChar_2</th>\n",
              "      <th>numSysChar_3</th>\n",
              "      <th>numSysChar_4</th>\n",
              "      <th>numSysChar_cupon</th>\n",
              "      <th>numSysChar_regular</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5</td>\n",
              "      <td>Friday</td>\n",
              "      <td>6.811315e+10</td>\n",
              "      <td>0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>81131</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7</td>\n",
              "      <td>Friday</td>\n",
              "      <td>6.794963e+10</td>\n",
              "      <td>2</td>\n",
              "      <td>13435.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>79496</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>Friday</td>\n",
              "      <td>4.259239e+11</td>\n",
              "      <td>30</td>\n",
              "      <td>58669.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>859233</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9</td>\n",
              "      <td>Friday</td>\n",
              "      <td>1.070088e+09</td>\n",
              "      <td>3</td>\n",
              "      <td>5719.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>10700</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10</td>\n",
              "      <td>Friday</td>\n",
              "      <td>1.700927e+10</td>\n",
              "      <td>3</td>\n",
              "      <td>10073.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>170092</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>2</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95669</th>\n",
              "      <td>191343</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>4.516225e+11</td>\n",
              "      <td>9</td>\n",
              "      <td>33991.0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>516223</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>5</td>\n",
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              "      <td>0</td>\n",
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              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95670</th>\n",
              "      <td>191344</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>1.614572e+11</td>\n",
              "      <td>5</td>\n",
              "      <td>15127.0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>314571</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95671</th>\n",
              "      <td>191345</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>1.191780e+11</td>\n",
              "      <td>17</td>\n",
              "      <td>49902.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>591775</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95672</th>\n",
              "      <td>191346</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>9.870798e+10</td>\n",
              "      <td>17</td>\n",
              "      <td>72908.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>687075</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95673</th>\n",
              "      <td>191347</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>7.990067e+09</td>\n",
              "      <td>2</td>\n",
              "      <td>5112.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>79900</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>95674 rows × 83 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       VisitNumber Weekday  ...  numSysChar_cupon  numSysChar_regular\n",
              "0                5  Friday  ...                 0                   1\n",
              "1                7  Friday  ...                 0                   2\n",
              "2                8  Friday  ...                 0                  20\n",
              "3                9  Friday  ...                 0                   3\n",
              "4               10  Friday  ...                 0                   3\n",
              "...            ...     ...  ...               ...                 ...\n",
              "95669       191343  Sunday  ...                 0                   7\n",
              "95670       191344  Sunday  ...                 0                   5\n",
              "95671       191345  Sunday  ...                 0                  11\n",
              "95672       191346  Sunday  ...                 0                  16\n",
              "95673       191347  Sunday  ...                 0                   2\n",
              "\n",
              "[95674 rows x 83 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1sQlxj55zUmH"
      },
      "source": [
        "clean_df = pd.get_dummies(clean_df, columns=[\"Weekday\"], dummy_na=False)"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1oZBpeY100Fv"
      },
      "source": [
        "### 2.8 spit (trainm, valid) y  test "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z_63zl92zUmK"
      },
      "source": [
        "df_test = clean_df[clean_df.is_train_set == 0]\n",
        "clean_df = clean_df[clean_df.is_train_set != 0]"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R8Zh_lHmzUmM"
      },
      "source": [
        "clean_df = clean_df.drop([\"is_train_set\"], axis=1)\n",
        "df_test = df_test.drop([\"is_train_set\"], axis=1)"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ePReaomezUmO"
      },
      "source": [
        "## 3 Models Train and Test \n",
        "---\n",
        "\n",
        "Load the data..."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pBK7etouzUmO"
      },
      "source": [
        "### Create the model and evaluate it\n",
        "\n",
        "split training dataset into train and \"validation\" \n",
        "(we won't be using validation set in this example, because of the cross-validation;\n",
        "\n",
        "but it could be useful for you depending on your approach)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1gs41buAzUmP"
      },
      "source": [
        "#state = np.random.RandomState(43)\n",
        "X_train, X_valid, y_train, y_valid = train_test_split(\n",
        "    clean_df, y, \n",
        "    test_size=0.2, \n",
        "    random_state=42)"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DzBeFoMDzUmS",
        "outputId": "afb4f6a6-5d40-4753-8dff-541148a5b354",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(X_train.shape, y_train.shape)"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(53623, 88) (53623,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QLe7m1VHzhsA"
      },
      "source": [
        "# results dataframe is used to store the computed results\n",
        "results = pd.DataFrame(columns=('clf', 'best_acc'))"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MM7rMaH45T3d"
      },
      "source": [
        "### 3.1 Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WwL0YQjtzUmf"
      },
      "source": [
        "tree_param = {\n",
        "    'criterion':('gini', 'entropy'),\n",
        "    'min_samples_leaf':(1, 2, 5),\n",
        "    'min_samples_split':(2, 3, 5, 10, 50, 100)}\n",
        "\n",
        "tree = DT(random_state=42)\n",
        "tree_clf = GridSearchCV(tree, tree_param, cv=3, scoring='accuracy', n_jobs=4) \n",
        "tree_clf.fit(X_train, y_train)\n",
        "best_tree_clf = tree_clf.best_estimator_"
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cRHII8FszUmh",
        "outputId": "99e553ea-bc76-4a5c-df7c-7cd8099545c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        }
      },
      "source": [
        "print(f'Best Decision Tree Test accuracy: = {best_tree_clf.score(X_valid, y_valid)}')\n",
        "print('Best Decision Tree accuracy: ', tree_clf.best_score_)\n",
        "print(best_tree_clf)\n",
        "results = results.append(\n",
        "    {'clf': best_tree_clf, 'best_acc': tree_clf.best_score_}, \n",
        "    ignore_index=True\n",
        "    )\n",
        "\n",
        "print('The best classifier so far is: ')\n",
        "print(results.loc[results['best_acc'].idxmax()]['clf'])"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Decision Tree Test accuracy: = 0.6545576607489184\n",
            "Best Decision Tree accuracy:  0.6395202762252108\n",
            "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
            "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
            "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                       min_samples_leaf=5, min_samples_split=100,\n",
            "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
            "                       random_state=42, splitter='best')\n",
            "The best classifier so far is: \n",
            "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
            "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
            "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                       min_samples_leaf=5, min_samples_split=100,\n",
            "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
            "                       random_state=42, splitter='best')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "del_OQH55Mc8"
      },
      "source": [
        "### 3.2  `RandomForestClassifier`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nmb1Db3zzUmc",
        "outputId": "4eb92b93-e44f-4496-f9ff-ed32150450d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "kfold = StratifiedKFold(n_splits=3, random_state=42, shuffle=True)\n",
        "rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "\n",
        "model_params = {\n",
        "    'min_samples_split': [2, 3, 4, 6],\n",
        "    'class_weight': ['balanced'],\n",
        "    'max_depth': [64, 96, 108, 128],\n",
        "    'bootstrap': [False],\n",
        "}\n",
        "\n",
        "rfor_clf = GridSearchCV(\n",
        "    rf_model, model_params, cv=kfold, scoring='accuracy', n_jobs=4\n",
        ")\n",
        "rfor_clf.fit(X_train, y_train)\n",
        "best_tree_clf = tree_clf.best_estimator_"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/joblib/externals/loky/process_executor.py:691: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
            "  \"timeout or by a memory leak.\", UserWarning\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VL6fttJwzUme",
        "outputId": "f4579923-461d-4e0a-9985-a06ca2db5734",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        }
      },
      "source": [
        "print(f'Best Random Forest Test accuracy = {best_tree_clf.score(X_valid, y_valid)}')\n",
        "print('Best Random Forest accuracy: ', rfor_clf.best_score_)\n",
        "print(best_tree_clf)\n",
        "results = results.append(\n",
        "    {'clf': best_tree_clf, 'best_acc': rfor_clf.best_score_}, \n",
        "    ignore_index=True\n",
        "    )\n",
        "\n",
        "print('The best classifier so far is: ')\n",
        "print(results.loc[results['best_acc'].idxmax()]['clf'])"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Random Forest Test accuracy = 0.6545576607489184\n",
            "Best Random Forest accuracy:  0.6884545404988507\n",
            "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
            "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
            "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                       min_samples_leaf=5, min_samples_split=100,\n",
            "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
            "                       random_state=42, splitter='best')\n",
            "The best classifier so far is: \n",
            "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
            "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
            "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                       min_samples_leaf=5, min_samples_split=100,\n",
            "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
            "                       random_state=42, splitter='best')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DjHvVJLEzUmU"
      },
      "source": [
        "### 3.2 `GradientBoostingClassifier` "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G9f0rYblzUmU"
      },
      "source": [
        "kfold = StratifiedKFold(n_splits=3, random_state=42, shuffle=True)\n",
        "\n",
        "parameters = {\n",
        "    'learning_rate': [0.1],\n",
        "    'loss': ['deviance'],\n",
        "    'min_samples_split': [2],\n",
        "    'max_depth': [3]\n",
        "}\n",
        "\n",
        "clf1 = GradientBoostingClassifier(random_state=42, n_estimators=100)\n",
        "boost_clf1 = GridSearchCV(clf1, parameters, cv=3, scoring='accuracy', n_jobs=4) \n",
        "\n",
        "boost_clf1.fit(X_train, y_train)\n",
        "best_tree_clf = boost_clf1.best_estimator_"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cB60B9dizUmW",
        "outputId": "2bc5fc75-41c3-4a4d-998c-3eac6fc16625",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        }
      },
      "source": [
        "print(f'Best Gradient Boosting Test accuracy = {best_tree_clf.score(X_valid, y_valid)}')\n",
        "print('Best Gradient Boosting accuracy: ', boost_clf1.best_score_)\n",
        "print(best_tree_clf)\n",
        "results = results.append(\n",
        "    {'clf': best_tree_clf, 'best_acc': boost_clf1.best_score_}, \n",
        "    ignore_index=True\n",
        "    )\n",
        "\n",
        "print('The best classifier so far is: ')\n",
        "print(results.loc[results['best_acc'].idxmax()]['clf'])"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Gradient Boosting Test accuracy = 0.705504997762196\n",
            "Best Gradient Boosting accuracy:  0.6470169330874266\n",
            "GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
            "                           learning_rate=0.1, loss='deviance', max_depth=3,\n",
            "                           max_features=None, max_leaf_nodes=None,\n",
            "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                           min_samples_leaf=1, min_samples_split=2,\n",
            "                           min_weight_fraction_leaf=0.0, n_estimators=100,\n",
            "                           n_iter_no_change=None, presort='deprecated',\n",
            "                           random_state=42, subsample=1.0, tol=0.0001,\n",
            "                           validation_fraction=0.1, verbose=0,\n",
            "                           warm_start=False)\n",
            "The best classifier so far is: \n",
            "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
            "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
            "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                       min_samples_leaf=5, min_samples_split=100,\n",
            "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
            "                       random_state=42, splitter='best')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-LLn1PbKztbj"
      },
      "source": [
        "#### 3.3 `XGradientBoostingClassifier` (XGBClassifier)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wRZoWW0iy7_B"
      },
      "source": [
        "kfold = StratifiedKFold(n_splits=3, random_state=42, shuffle=True)\n",
        "\n",
        "xgbc = XGBClassifier(random_state=42)\n",
        "parameters = {\n",
        "    'eta': [0.1],\n",
        "}\n",
        "xgbc_clf = GridSearchCV(\n",
        "    xgbc, parameters, cv=kfold, scoring='balanced_accuracy', n_jobs=4)\n",
        "\n",
        "xgbc_clf.fit(X_train, y_train)\n",
        "best_tree_clf = xgbc_clf.best_estimator_"
      ],
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZpQEreNAy9U9",
        "outputId": "b3988c72-7163-4851-bb3f-2b177c28ee8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "source": [
        "best_tree_clf"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "              colsample_bynode=1, colsample_bytree=1, eta=0.1, gamma=0,\n",
              "              learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
              "              min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,\n",
              "              nthread=None, objective='multi:softprob', random_state=42,\n",
              "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "              silent=None, subsample=1, verbosity=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cU2-oiXDy_9W",
        "outputId": "6a753739-5b19-4740-ea39-26b05e1bddf8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y_pred = best_tree_clf.predict(X_valid)\n",
        "predictions = [value for value in y_pred]\n",
        "\n",
        "accuracy = accuracy_score(y_valid, predictions)\n",
        "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))\n",
        "\n",
        "# print('Best GradientBoostingClassifier: ', best_tree_clf.best_score_)"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 70.05%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UxNBnycpzUmj",
        "outputId": "982b01bd-bf0e-4f1c-b4ea-8e71cfd1b860",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# sns.set_context(context='talk', font_scale=0.5)\n",
        "fig = plt.figure(figsize=(25,25))\n",
        "ax = plt.subplot('111')\n",
        "plot_confusion_matrix(\n",
        "    best_tree_clf, X_valid, y_valid,\n",
        "    cmap=plt.cm.Blues,\n",
        "    normalize='true',\n",
        "    ax=ax\n",
        "    )\n",
        "plt.title('Confusion Matrix SGDClassifier best model')\n",
        "plt.show()"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "display_data",
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            "text/plain": [
              "<Figure size 1800x1800 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9gR_VeFQzUmm"
      },
      "source": [
        "## 4 Results write back\n",
        "---\n",
        "\n",
        "the best model is:\n",
        "for XGBClassifier Accuracy: `70.05%`\n",
        "\n",
        "and \n",
        "\n",
        "\n",
        "\n",
        "```python\n",
        "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
        "              colsample_bynode=1, colsample_bytree=1, eta=0.1, gamma=0,\n",
        "              gpu_id=-1, importance_type='gain', interaction_constraints='',\n",
        "              learning_rate=0.100000001, max_delta_step=0, max_depth=6,\n",
        "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
        "              n_estimators=100, n_jobs=0, num_parallel_tree=1,\n",
        "              objective='multi:softprob', random_state=42, reg_alpha=0,\n",
        "              reg_lambda=1, scale_pos_weight=None, subsample=1,\n",
        "              tree_method='exact', validate_parameters=1, verbosity=None)\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WUpWHqCrzUmn",
        "outputId": "8e8663e6-9faf-4f43-eacd-3e128a29326b"
      },
      "source": [
        "# Esto hace un ranking de la importancia de la variable para el modelo\n",
        "# lo saque de aca https://www.kaggle.com/zlatankr/titanic-random-forest-82-78/data\n",
        "\n",
        "pd.concat((\n",
        "    pd.DataFrame(X_train.columns, columns = ['variable']), \n",
        "    pd.DataFrame(\n",
        "        best_tree_clf.feature_importances_, columns = ['importance'])), \n",
        "    axis=1\n",
        ").sort_values(by='importance', ascending=False)[:20]\n"
      ],
      "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>variable</th>\n",
              "      <th>importance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ScanCount</td>\n",
              "      <td>0.394885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>FinelineNumber</td>\n",
              "      <td>0.126550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Upc</td>\n",
              "      <td>0.076435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>DepartmentDescription_FINANCIAL SERVICES</td>\n",
              "      <td>0.031606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>DepartmentDescription_PHARMACY RX</td>\n",
              "      <td>0.025338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>DepartmentDescription_DSD GROCERY</td>\n",
              "      <td>0.024775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>DepartmentDescription_PERSONAL CARE</td>\n",
              "      <td>0.024224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>DepartmentDescription_MENS WEAR</td>\n",
              "      <td>0.018774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>DepartmentDescription_PRODUCE</td>\n",
              "      <td>0.018364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>DepartmentDescription_PHARMACY OTC</td>\n",
              "      <td>0.017638</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>DepartmentDescription_SERVICE DELI</td>\n",
              "      <td>0.015215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>DepartmentDescription_DAIRY</td>\n",
              "      <td>0.014892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>DepartmentDescription_GROCERY DRY GOODS</td>\n",
              "      <td>0.012861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>DepartmentDescription_INFANT CONSUMABLE HARDLINES</td>\n",
              "      <td>0.009997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>DepartmentDescription_HOUSEHOLD CHEMICALS/SUPP</td>\n",
              "      <td>0.009980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>DepartmentDescription_PLAYERS AND ELECTRONICS</td>\n",
              "      <td>0.009462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>DepartmentDescription_ELECTRONICS</td>\n",
              "      <td>0.008900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>DepartmentDescription_BEAUTY</td>\n",
              "      <td>0.008728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>DepartmentDescription_LADIESWEAR</td>\n",
              "      <td>0.008208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>DepartmentDescription_MEAT - FRESH &amp; FROZEN</td>\n",
              "      <td>0.008112</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                             variable  importance\n",
              "2                                           ScanCount    0.394885\n",
              "3                                      FinelineNumber    0.126550\n",
              "1                                                 Upc    0.076435\n",
              "24           DepartmentDescription_FINANCIAL SERVICES    0.031606\n",
              "57                  DepartmentDescription_PHARMACY RX    0.025338\n",
              "21                  DepartmentDescription_DSD GROCERY    0.024775\n",
              "54                DepartmentDescription_PERSONAL CARE    0.024224\n",
              "47                    DepartmentDescription_MENS WEAR    0.018774\n",
              "61                      DepartmentDescription_PRODUCE    0.018364\n",
              "56                 DepartmentDescription_PHARMACY OTC    0.017638\n",
              "64                 DepartmentDescription_SERVICE DELI    0.015215\n",
              "20                        DepartmentDescription_DAIRY    0.014892\n",
              "28            DepartmentDescription_GROCERY DRY GOODS    0.012861\n",
              "38  DepartmentDescription_INFANT CONSUMABLE HARDLINES    0.009997\n",
              "34     DepartmentDescription_HOUSEHOLD CHEMICALS/SUPP    0.009980\n",
              "58      DepartmentDescription_PLAYERS AND ELECTRONICS    0.009462\n",
              "22                  DepartmentDescription_ELECTRONICS    0.008900\n",
              "9                        DepartmentDescription_BEAUTY    0.008728\n",
              "41                   DepartmentDescription_LADIESWEAR    0.008208\n",
              "45        DepartmentDescription_MEAT - FRESH & FROZEN    0.008112"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ZBP5qM2zUmo"
      },
      "source": [
        "# yy = results.clf.iloc[0].predict(XX)\n",
        "best_tree_clf = boost_clf3.best_estimator_\n",
        "yy = best_tree_clf.predict(df_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Oo_z1b6zUmq",
        "outputId": "ef7bcd83-8d3f-4f59-d912-dfbcc24c1620"
      },
      "source": [
        "submission = pd.DataFrame(\n",
        "    list(zip(df_test.VisitNumber, yy)), \n",
        "    columns=[\"VisitNumber\", \"TripType\"])\n",
        "\n",
        "submission"
      ],
      "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>VisitNumber</th>\n",
              "      <th>TripType</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>23</td>\n",
              "      <td>999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28640</th>\n",
              "      <td>191331</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28641</th>\n",
              "      <td>191335</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28642</th>\n",
              "      <td>191342</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28643</th>\n",
              "      <td>191345</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28644</th>\n",
              "      <td>191346</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>28645 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       VisitNumber  TripType\n",
              "0                7         8\n",
              "1                8        26\n",
              "2               15        21\n",
              "3               19        25\n",
              "4               23       999\n",
              "...            ...       ...\n",
              "28640       191331         9\n",
              "28641       191335        32\n",
              "28642       191342         8\n",
              "28643       191345        39\n",
              "28644       191346        39\n",
              "\n",
              "[28645 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mA0rmfm7zUmr"
      },
      "source": [
        "# submission.to_csv(\"../data/submission.csv\", header=True, index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c_i_TW2TzUmt"
      },
      "source": [
        "---\n",
        "## End"
      ]
    }
  ]
}