TimeSeries_BY_Hackathon.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "6sv6upW03fXM"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import copy as cp\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder,LabelEncoder,MinMaxScaler\n",
    "from sklearn.compose import make_column_transformer\n",
    "import category_encoders as ce\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from matplotlib import pyplot\n",
    "from sklearn.model_selection import train_test_split\n",
    "#-------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "5-RUiC3v42kc"
   },
   "outputs": [],
   "source": [
    "mainpath='https://raw.githubusercontent.com/RudraRudra123/Predict_Order_Qty_Changes/master/Data/'\n",
    "eso_dataset = pd.read_csv(mainpath+'Predictive%20Modelling%20-%20Data%20file-Train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GpTxgtVZ5WvR",
    "outputId": "563428d0-cec2-46e8-c26a-6dcf14e57d63"
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>order_date</th>\n",
       "      <th>order_day_of_week</th>\n",
       "      <th>store</th>\n",
       "      <th>supplier_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>category</th>\n",
       "      <th>on_order_quantity</th>\n",
       "      <th>on_hand_quantity</th>\n",
       "      <th>product_case_pack</th>\n",
       "      <th>retail_price</th>\n",
       "      <th>replenishment_unit_cost</th>\n",
       "      <th>forecast_start_date</th>\n",
       "      <th>forecast_end_date</th>\n",
       "      <th>Ao_Forecast_Quantity</th>\n",
       "      <th>Minimum_Order_Quantity</th>\n",
       "      <th>Maximum_Order_Quantity</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>suggested_order_quantity</th>\n",
       "      <th>actual_order_quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>Monday</td>\n",
       "      <td>s1</td>\n",
       "      <td>50</td>\n",
       "      <td>28483</td>\n",
       "      <td>c654</td>\n",
       "      <td>0</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>16</td>\n",
       "      <td>2.60</td>\n",
       "      <td>18.56</td>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>1/8/2017</td>\n",
       "      <td>8.50</td>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>Monday</td>\n",
       "      <td>s1</td>\n",
       "      <td>50</td>\n",
       "      <td>40917</td>\n",
       "      <td>c896</td>\n",
       "      <td>0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>8</td>\n",
       "      <td>2.95</td>\n",
       "      <td>11.28</td>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>1/8/2017</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>Monday</td>\n",
       "      <td>s1</td>\n",
       "      <td>50</td>\n",
       "      <td>136366</td>\n",
       "      <td>c896</td>\n",
       "      <td>0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>6</td>\n",
       "      <td>2.50</td>\n",
       "      <td>4.76</td>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>1/8/2017</td>\n",
       "      <td>0.80</td>\n",
       "      <td>5</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>Monday</td>\n",
       "      <td>s1</td>\n",
       "      <td>50</td>\n",
       "      <td>136344</td>\n",
       "      <td>c762</td>\n",
       "      <td>0</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>8</td>\n",
       "      <td>3.95</td>\n",
       "      <td>15.04</td>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>1/8/2017</td>\n",
       "      <td>1.55</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>Monday</td>\n",
       "      <td>s1</td>\n",
       "      <td>50</td>\n",
       "      <td>136078</td>\n",
       "      <td>c672</td>\n",
       "      <td>0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>4</td>\n",
       "      <td>2.80</td>\n",
       "      <td>4.56</td>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>1/8/2017</td>\n",
       "      <td>1.15</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id order_date order_day_of_week store  supplier_id  product_id category  \\\n",
       "0   1   1/2/2017            Monday    s1           50       28483     c654   \n",
       "1   2   1/2/2017            Monday    s1           50       40917     c896   \n",
       "2   3   1/2/2017            Monday    s1           50      136366     c896   \n",
       "3   4   1/2/2017            Monday    s1           50      136344     c762   \n",
       "4   5   1/2/2017            Monday    s1           50      136078     c672   \n",
       "\n",
       "   on_order_quantity  on_hand_quantity  product_case_pack  retail_price  \\\n",
       "0                  0          0.625000                 16          2.60   \n",
       "1                  0          0.125000                  8          2.95   \n",
       "2                  0          0.833333                  6          2.50   \n",
       "3                  0          0.375000                  8          3.95   \n",
       "4                  0          0.750000                  4          2.80   \n",
       "\n",
       "   replenishment_unit_cost forecast_start_date forecast_end_date  \\\n",
       "0                    18.56            1/2/2017          1/8/2017   \n",
       "1                    11.28            1/2/2017          1/8/2017   \n",
       "2                     4.76            1/2/2017          1/8/2017   \n",
       "3                    15.04            1/2/2017          1/8/2017   \n",
       "4                     4.56            1/2/2017          1/8/2017   \n",
       "\n",
       "   Ao_Forecast_Quantity  Minimum_Order_Quantity  Maximum_Order_Quantity  \\\n",
       "0                  8.50                       2                      48   \n",
       "1                  1.95                       2                      20   \n",
       "2                  0.80                       5                      40   \n",
       "3                  1.55                       2                      20   \n",
       "4                  1.15                       2                      12   \n",
       "\n",
       "   lead_time  suggested_order_quantity  actual_order_quantity  \n",
       "0          2                         1                    1.0  \n",
       "1          2                         1                    1.0  \n",
       "2          2                         1                    2.0  \n",
       "3          2                         1                    1.0  \n",
       "4          2                         1                    1.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eso_dataset.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LuMneKi35lmb",
    "outputId": "8cc13b20-8b47-4280-e0fb-10b59ace0233"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'order_date', 'order_day_of_week', 'store', 'supplier_id',\n",
       "       'product_id', 'category', 'on_order_quantity', 'on_hand_quantity',\n",
       "       'product_case_pack', 'retail_price', 'replenishment_unit_cost',\n",
       "       'forecast_start_date', 'forecast_end_date', 'Ao_Forecast_Quantity',\n",
       "       'Minimum_Order_Quantity', 'Maximum_Order_Quantity', 'lead_time',\n",
       "       'suggested_order_quantity', 'actual_order_quantity'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eso_dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19668 entries, 0 to 19667\n",
      "Data columns (total 20 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   id                        19668 non-null  int64  \n",
      " 1   order_date                19668 non-null  object \n",
      " 2   order_day_of_week         19668 non-null  object \n",
      " 3   store                     19668 non-null  object \n",
      " 4   supplier_id               19668 non-null  int64  \n",
      " 5   product_id                19668 non-null  int64  \n",
      " 6   category                  19668 non-null  object \n",
      " 7   on_order_quantity         19668 non-null  int64  \n",
      " 8   on_hand_quantity          19668 non-null  float64\n",
      " 9   product_case_pack         19668 non-null  int64  \n",
      " 10  retail_price              19668 non-null  float64\n",
      " 11  replenishment_unit_cost   19668 non-null  float64\n",
      " 12  forecast_start_date       19668 non-null  object \n",
      " 13  forecast_end_date         19668 non-null  object \n",
      " 14  Ao_Forecast_Quantity      19668 non-null  float64\n",
      " 15  Minimum_Order_Quantity    19668 non-null  int64  \n",
      " 16  Maximum_Order_Quantity    19668 non-null  int64  \n",
      " 17  lead_time                 19668 non-null  int64  \n",
      " 18  suggested_order_quantity  19668 non-null  int64  \n",
      " 19  actual_order_quantity     19668 non-null  float64\n",
      "dtypes: float64(5), int64(9), object(6)\n",
      "memory usage: 3.0+ MB\n"
     ]
    }
   ],
   "source": [
    "eso_dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XyQeroS_A6T0",
    "outputId": "8dd80d8c-3480-4fc3-86e7-fd3f4273aa13"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Week day unique values: ['Monday' 'Wednesday' 'Thursday' 'Friday' 'Saturday' 'Tuesday' 'Sunday']\n",
      "store unique values ['s1' 's2']\n"
     ]
    }
   ],
   "source": [
    "print('Week day unique values:', eso_dataset.order_day_of_week.unique())\n",
    "print('store unique values', eso_dataset.store.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "NMwXHDSxfrlI"
   },
   "outputs": [],
   "source": [
    "store_data = cp.deepcopy(eso_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set Date Field types to Date fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 202
    },
    "id": "zPcq8Tz0OX2E",
    "outputId": "86d1b982-8a7d-4894-a489-9a4ace2b11d8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>order_date</th>\n",
       "      <th>forecast_start_date</th>\n",
       "      <th>forecast_end_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>1/8/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>1/8/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>1/8/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>1/8/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>1/8/2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  order_date forecast_start_date forecast_end_date\n",
       "0   1/2/2017            1/2/2017          1/8/2017\n",
       "1   1/2/2017            1/2/2017          1/8/2017\n",
       "2   1/2/2017            1/2/2017          1/8/2017\n",
       "3   1/2/2017            1/2/2017          1/8/2017\n",
       "4   1/2/2017            1/2/2017          1/8/2017"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_data[['order_date', 'forecast_start_date', 'forecast_end_date']][0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "EpDUCc3zfW_z"
   },
   "outputs": [],
   "source": [
    "store_data.order_date = pd.to_datetime(store_data.order_date)\n",
    "store_data.forecast_start_date = pd.to_datetime(store_data.forecast_start_date)\n",
    "store_data.forecast_end_date = pd.to_datetime(store_data.forecast_end_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 202
    },
    "id": "v_WYNetnglkj",
    "outputId": "7d4de73a-4c4b-4c5b-8d3f-383987c28c34"
   },
   "outputs": [
    {
     "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>order_date</th>\n",
       "      <th>forecast_start_date</th>\n",
       "      <th>forecast_end_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>2017-01-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>2017-01-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>2017-01-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>2017-01-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>2017-01-08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  order_date forecast_start_date forecast_end_date\n",
       "0 2017-01-02          2017-01-02        2017-01-08\n",
       "1 2017-01-02          2017-01-02        2017-01-08\n",
       "2 2017-01-02          2017-01-02        2017-01-08\n",
       "3 2017-01-02          2017-01-02        2017-01-08\n",
       "4 2017-01-02          2017-01-02        2017-01-08"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_data[['order_date', 'forecast_start_date', 'forecast_end_date']][0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1Kap1PPwpslb"
   },
   "source": [
    "### Check if there are nulls:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "k2WjEgUthxLS",
    "outputId": "e2133168-ea28-4834-e4d5-30c1ac41c73c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_data.columns[store_data.isnull().any()].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VinOqduEqUO6"
   },
   "source": [
    "#### There are no NULLS in the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before changes to pack size\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>order_date</th>\n",
       "      <th>order_day_of_week</th>\n",
       "      <th>store</th>\n",
       "      <th>supplier_id</th>\n",
       "      <th>product_id</th>\n",
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       "      <th>product_case_pack</th>\n",
       "      <th>retail_price</th>\n",
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       "      <th>forecast_start_date</th>\n",
       "      <th>forecast_end_date</th>\n",
       "      <th>Ao_Forecast_Quantity</th>\n",
       "      <th>Minimum_Order_Quantity</th>\n",
       "      <th>Maximum_Order_Quantity</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>suggested_order_quantity</th>\n",
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       "      <th>0</th>\n",
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       "      <td>16</td>\n",
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       "      <td>8.50</td>\n",
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       "      <td>2</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>Monday</td>\n",
       "      <td>s1</td>\n",
       "      <td>50</td>\n",
       "      <td>40917</td>\n",
       "      <td>c896</td>\n",
       "      <td>0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>8</td>\n",
       "      <td>2.95</td>\n",
       "      <td>11.28</td>\n",
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       "      <td>4.76</td>\n",
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       "      <td>136344</td>\n",
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       "      <td>s1</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   id order_date order_day_of_week store  supplier_id  product_id category  \\\n",
       "0   1 2017-01-02            Monday    s1           50       28483     c654   \n",
       "1   2 2017-01-02            Monday    s1           50       40917     c896   \n",
       "2   3 2017-01-02            Monday    s1           50      136366     c896   \n",
       "3   4 2017-01-02            Monday    s1           50      136344     c762   \n",
       "4   5 2017-01-02            Monday    s1           50      136078     c672   \n",
       "\n",
       "   on_order_quantity  on_hand_quantity  product_case_pack  retail_price  \\\n",
       "0                  0          0.625000                 16          2.60   \n",
       "1                  0          0.125000                  8          2.95   \n",
       "2                  0          0.833333                  6          2.50   \n",
       "3                  0          0.375000                  8          3.95   \n",
       "4                  0          0.750000                  4          2.80   \n",
       "\n",
       "   replenishment_unit_cost forecast_start_date forecast_end_date  \\\n",
       "0                    18.56          2017-01-02        2017-01-08   \n",
       "1                    11.28          2017-01-02        2017-01-08   \n",
       "2                     4.76          2017-01-02        2017-01-08   \n",
       "3                    15.04          2017-01-02        2017-01-08   \n",
       "4                     4.56          2017-01-02        2017-01-08   \n",
       "\n",
       "   Ao_Forecast_Quantity  Minimum_Order_Quantity  Maximum_Order_Quantity  \\\n",
       "0                  8.50                       2                      48   \n",
       "1                  1.95                       2                      20   \n",
       "2                  0.80                       5                      40   \n",
       "3                  1.55                       2                      20   \n",
       "4                  1.15                       2                      12   \n",
       "\n",
       "   lead_time  suggested_order_quantity  actual_order_quantity  \n",
       "0          2                         1                    1.0  \n",
       "1          2                         1                    1.0  \n",
       "2          2                         1                    2.0  \n",
       "3          2                         1                    1.0  \n",
       "4          2                         1                    1.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Before changes to pack size')\n",
    "store_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting data columns into Pack size \n",
    "def topacksize(store_data):\n",
    "    store_data.Ao_Forecast_Quantity   = store_data.Ao_Forecast_Quantity/store_data.product_case_pack\n",
    "    store_data.Minimum_Order_Quantity = store_data.Minimum_Order_Quantity/store_data.product_case_pack\n",
    "    store_data.Maximum_Order_Quantity = store_data.Maximum_Order_Quantity/store_data.product_case_pack\n",
    "    return store_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g0A17f7ULZel"
   },
   "source": [
    "## Inspect category variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fiPl-CaYqTyJ",
    "outputId": "eb753256-acb8-4aab-c874-4a84de823043"
   },
   "outputs": [],
   "source": [
    "for index, column in enumerate(['order_day_of_week','store','supplier_id','category',]):\n",
    "  # print(column)\n",
    "  print(column,':   ',store_data[column].unique())\n",
    "\n",
    "print('\\n other variable is product_id, not printed here as the list is big')\n",
    "print('total number of unique products:', len(pd.unique(store_data['product_id'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visual Interpretatin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 668
    },
    "id": "8Gmi2zMVmFX3",
    "outputId": "207b099c-2c25-4682-9050-32c611a924d4"
   },
   "outputs": [],
   "source": [
    "matrix = store_data.corr() \n",
    "f, ax = plt.subplots(figsize=(10, 10)) \n",
    "sns.heatmap(matrix, vmax=.8, square=True, cmap=\"OrRd\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 448
    },
    "id": "ioHv6eHgnPoZ",
    "outputId": "5366f403-43d2-479f-9660-76aaa92c50eb"
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(25,8))\n",
    "sns.lineplot(data=store_data, x=\"order_date\", y=\"suggested_order_quantity\")\n",
    "sns.lineplot(data=store_data, x=\"order_date\", y=\"actual_order_quantity\",ci=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 447
    },
    "id": "5aDVn8CggNzh",
    "outputId": "4969ef7e-3cc8-4887-e2ee-e981b8623903"
   },
   "outputs": [],
   "source": [
    "store_data_visualize = pd.DataFrame()\n",
    "plt.figure(figsize=(25,8))\n",
    "sns.set_style('whitegrid')\n",
    "store_data_visualize['order_qty_deviation'] = store_data['actual_order_quantity'] - store_data['suggested_order_quantity']\n",
    "store_data_visualize['order_date'] = store_data['order_date']\n",
    "sns.lineplot(data=store_data_visualize, x=\"order_date\", y ='order_qty_deviation', ci=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZJk8y8GMVjbm"
   },
   "source": [
    "#### Observation: Adjustments are on both sides. Amendments to order quantity is a frequent activity "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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       "      <td>3.95</td>\n",
       "      <td>8.24</td>\n",
       "      <td>2018-06-28</td>\n",
       "      <td>2018-07-04</td>\n",
       "      <td>5.270833</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19666</th>\n",
       "      <td>19667</td>\n",
       "      <td>2018-06-28</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>s2</td>\n",
       "      <td>50</td>\n",
       "      <td>143930</td>\n",
       "      <td>c676</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>2.90</td>\n",
       "      <td>7.26</td>\n",
       "      <td>2018-06-28</td>\n",
       "      <td>2018-07-04</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19667</th>\n",
       "      <td>19668</td>\n",
       "      <td>2018-06-28</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>s2</td>\n",
       "      <td>50</td>\n",
       "      <td>140198</td>\n",
       "      <td>c762</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>3.80</td>\n",
       "      <td>17.60</td>\n",
       "      <td>2018-06-28</td>\n",
       "      <td>2018-07-04</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19668 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id order_date order_day_of_week store  supplier_id  product_id  \\\n",
       "0          1 2017-01-02            Monday    s1           50       28483   \n",
       "1          2 2017-01-02            Monday    s1           50       40917   \n",
       "2          3 2017-01-02            Monday    s1           50      136366   \n",
       "3          4 2017-01-02            Monday    s1           50      136344   \n",
       "4          5 2017-01-02            Monday    s1           50      136078   \n",
       "...      ...        ...               ...   ...          ...         ...   \n",
       "19663  19664 2018-06-28          Thursday    s2           50      136340   \n",
       "19664  19665 2018-06-28          Thursday    s2           50      112932   \n",
       "19665  19666 2018-06-28          Thursday    s2           50       32944   \n",
       "19666  19667 2018-06-28          Thursday    s2           50      143930   \n",
       "19667  19668 2018-06-28          Thursday    s2           50      140198   \n",
       "\n",
       "      category  on_order_quantity  on_hand_quantity  product_case_pack  \\\n",
       "0         c654                  0          0.625000                 16   \n",
       "1         c896                  0          0.125000                  8   \n",
       "2         c896                  0          0.833333                  6   \n",
       "3         c762                  0          0.375000                  8   \n",
       "4         c672                  0          0.750000                  4   \n",
       "...        ...                ...               ...                ...   \n",
       "19663    c2052                  1          2.000000                  4   \n",
       "19664     c770                  0          1.400000                 10   \n",
       "19665     c790                  2          3.625000                  8   \n",
       "19666     c676                  0          0.000000                  6   \n",
       "19667     c762                  0          1.000000                 10   \n",
       "\n",
       "       retail_price  replenishment_unit_cost forecast_start_date  \\\n",
       "0              2.60                    18.56          2017-01-02   \n",
       "1              2.95                    11.28          2017-01-02   \n",
       "2              2.50                     4.76          2017-01-02   \n",
       "3              3.95                    15.04          2017-01-02   \n",
       "4              2.80                     4.56          2017-01-02   \n",
       "...             ...                      ...                 ...   \n",
       "19663          5.95                     9.36          2018-06-28   \n",
       "19664          3.50                    13.90          2018-06-28   \n",
       "19665          3.95                     8.24          2018-06-28   \n",
       "19666          2.90                     7.26          2018-06-28   \n",
       "19667          3.80                    17.60          2018-06-28   \n",
       "\n",
       "      forecast_end_date  Ao_Forecast_Quantity  Minimum_Order_Quantity  \\\n",
       "0            2017-01-08              0.531250                0.125000   \n",
       "1            2017-01-08              0.243750                0.250000   \n",
       "2            2017-01-08              0.133333                0.833333   \n",
       "3            2017-01-08              0.193750                0.250000   \n",
       "4            2017-01-08              0.287500                0.500000   \n",
       "...                 ...                   ...                     ...   \n",
       "19663        2018-07-04              1.900000                1.500000   \n",
       "19664        2018-07-04              1.113333                0.400000   \n",
       "19665        2018-07-04              5.270833                0.500000   \n",
       "19666        2018-07-04              0.500000                0.666667   \n",
       "19667        2018-07-04              0.390000                0.800000   \n",
       "\n",
       "       Maximum_Order_Quantity  lead_time  suggested_order_quantity  \\\n",
       "0                    3.000000          2                         1   \n",
       "1                    2.500000          2                         1   \n",
       "2                    6.666667          2                         1   \n",
       "3                    2.500000          2                         1   \n",
       "4                    3.000000          2                         1   \n",
       "...                       ...        ...                       ...   \n",
       "19663                3.000000          3                         1   \n",
       "19664                2.000000          3                         1   \n",
       "19665                2.500000          3                         1   \n",
       "19666                2.000000          3                         2   \n",
       "19667                2.000000          3                         1   \n",
       "\n",
       "       actual_order_quantity  \n",
       "0                        1.0  \n",
       "1                        1.0  \n",
       "2                        2.0  \n",
       "3                        1.0  \n",
       "4                        1.0  \n",
       "...                      ...  \n",
       "19663                    1.0  \n",
       "19664                    1.0  \n",
       "19665                    1.0  \n",
       "19666                    2.0  \n",
       "19667                    1.0  \n",
       "\n",
       "[19668 rows x 20 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topacksize(store_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outlier Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>order_date</th>\n",
       "      <th>order_day_of_week</th>\n",
       "      <th>store</th>\n",
       "      <th>supplier_id</th>\n",
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       "      <th>category</th>\n",
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       "      <th>on_hand_quantity</th>\n",
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       "      <th>retail_price</th>\n",
       "      <th>replenishment_unit_cost</th>\n",
       "      <th>forecast_start_date</th>\n",
       "      <th>forecast_end_date</th>\n",
       "      <th>Ao_Forecast_Quantity</th>\n",
       "      <th>Minimum_Order_Quantity</th>\n",
       "      <th>Maximum_Order_Quantity</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>suggested_order_quantity</th>\n",
       "      <th>actual_order_quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>Monday</td>\n",
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       "      <td>50</td>\n",
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       "      <td>16</td>\n",
       "      <td>2.60</td>\n",
       "      <td>18.56</td>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>2017-01-08</td>\n",
       "      <td>0.531250</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>Monday</td>\n",
       "      <td>s1</td>\n",
       "      <td>50</td>\n",
       "      <td>40917</td>\n",
       "      <td>c896</td>\n",
       "      <td>0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>8</td>\n",
       "      <td>2.95</td>\n",
       "      <td>11.28</td>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>2017-01-08</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>Monday</td>\n",
       "      <td>s1</td>\n",
       "      <td>50</td>\n",
       "      <td>136366</td>\n",
       "      <td>c896</td>\n",
       "      <td>0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>6</td>\n",
       "      <td>2.50</td>\n",
       "      <td>4.76</td>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>2017-01-08</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>Monday</td>\n",
       "      <td>s1</td>\n",
       "      <td>50</td>\n",
       "      <td>136344</td>\n",
       "      <td>c762</td>\n",
       "      <td>0</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>8</td>\n",
       "      <td>3.95</td>\n",
       "      <td>15.04</td>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>2017-01-08</td>\n",
       "      <td>0.193750</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>Monday</td>\n",
       "      <td>s1</td>\n",
       "      <td>50</td>\n",
       "      <td>136078</td>\n",
       "      <td>c672</td>\n",
       "      <td>0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>4</td>\n",
       "      <td>2.80</td>\n",
       "      <td>4.56</td>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>2017-01-08</td>\n",
       "      <td>0.287500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>19663</th>\n",
       "      <td>19664</td>\n",
       "      <td>2018-06-28</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>s2</td>\n",
       "      <td>50</td>\n",
       "      <td>136340</td>\n",
       "      <td>c2052</td>\n",
       "      <td>1</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>5.95</td>\n",
       "      <td>9.36</td>\n",
       "      <td>2018-06-28</td>\n",
       "      <td>2018-07-04</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19664</th>\n",
       "      <td>19665</td>\n",
       "      <td>2018-06-28</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>s2</td>\n",
       "      <td>50</td>\n",
       "      <td>112932</td>\n",
       "      <td>c770</td>\n",
       "      <td>0</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>10</td>\n",
       "      <td>3.50</td>\n",
       "      <td>13.90</td>\n",
       "      <td>2018-06-28</td>\n",
       "      <td>2018-07-04</td>\n",
       "      <td>1.113333</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19665</th>\n",
       "      <td>19666</td>\n",
       "      <td>2018-06-28</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>s2</td>\n",
       "      <td>50</td>\n",
       "      <td>32944</td>\n",
       "      <td>c790</td>\n",
       "      <td>2</td>\n",
       "      <td>3.625000</td>\n",
       "      <td>8</td>\n",
       "      <td>3.95</td>\n",
       "      <td>8.24</td>\n",
       "      <td>2018-06-28</td>\n",
       "      <td>2018-07-04</td>\n",
       "      <td>5.270833</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19666</th>\n",
       "      <td>19667</td>\n",
       "      <td>2018-06-28</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>s2</td>\n",
       "      <td>50</td>\n",
       "      <td>143930</td>\n",
       "      <td>c676</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>2.90</td>\n",
       "      <td>7.26</td>\n",
       "      <td>2018-06-28</td>\n",
       "      <td>2018-07-04</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19667</th>\n",
       "      <td>19668</td>\n",
       "      <td>2018-06-28</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>s2</td>\n",
       "      <td>50</td>\n",
       "      <td>140198</td>\n",
       "      <td>c762</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>3.80</td>\n",
       "      <td>17.60</td>\n",
       "      <td>2018-06-28</td>\n",
       "      <td>2018-07-04</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19668 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id order_date order_day_of_week store  supplier_id  product_id  \\\n",
       "0          1 2017-01-02            Monday    s1           50       28483   \n",
       "1          2 2017-01-02            Monday    s1           50       40917   \n",
       "2          3 2017-01-02            Monday    s1           50      136366   \n",
       "3          4 2017-01-02            Monday    s1           50      136344   \n",
       "4          5 2017-01-02            Monday    s1           50      136078   \n",
       "...      ...        ...               ...   ...          ...         ...   \n",
       "19663  19664 2018-06-28          Thursday    s2           50      136340   \n",
       "19664  19665 2018-06-28          Thursday    s2           50      112932   \n",
       "19665  19666 2018-06-28          Thursday    s2           50       32944   \n",
       "19666  19667 2018-06-28          Thursday    s2           50      143930   \n",
       "19667  19668 2018-06-28          Thursday    s2           50      140198   \n",
       "\n",
       "      category  on_order_quantity  on_hand_quantity  product_case_pack  \\\n",
       "0         c654                  0          0.625000                 16   \n",
       "1         c896                  0          0.125000                  8   \n",
       "2         c896                  0          0.833333                  6   \n",
       "3         c762                  0          0.375000                  8   \n",
       "4         c672                  0          0.750000                  4   \n",
       "...        ...                ...               ...                ...   \n",
       "19663    c2052                  1          2.000000                  4   \n",
       "19664     c770                  0          1.400000                 10   \n",
       "19665     c790                  2          3.625000                  8   \n",
       "19666     c676                  0          0.000000                  6   \n",
       "19667     c762                  0          1.000000                 10   \n",
       "\n",
       "       retail_price  replenishment_unit_cost forecast_start_date  \\\n",
       "0              2.60                    18.56          2017-01-02   \n",
       "1              2.95                    11.28          2017-01-02   \n",
       "2              2.50                     4.76          2017-01-02   \n",
       "3              3.95                    15.04          2017-01-02   \n",
       "4              2.80                     4.56          2017-01-02   \n",
       "...             ...                      ...                 ...   \n",
       "19663          5.95                     9.36          2018-06-28   \n",
       "19664          3.50                    13.90          2018-06-28   \n",
       "19665          3.95                     8.24          2018-06-28   \n",
       "19666          2.90                     7.26          2018-06-28   \n",
       "19667          3.80                    17.60          2018-06-28   \n",
       "\n",
       "      forecast_end_date  Ao_Forecast_Quantity  Minimum_Order_Quantity  \\\n",
       "0            2017-01-08              0.531250                0.125000   \n",
       "1            2017-01-08              0.243750                0.250000   \n",
       "2            2017-01-08              0.133333                0.833333   \n",
       "3            2017-01-08              0.193750                0.250000   \n",
       "4            2017-01-08              0.287500                0.500000   \n",
       "...                 ...                   ...                     ...   \n",
       "19663        2018-07-04              1.900000                1.500000   \n",
       "19664        2018-07-04              1.113333                0.400000   \n",
       "19665        2018-07-04              5.270833                0.500000   \n",
       "19666        2018-07-04              0.500000                0.666667   \n",
       "19667        2018-07-04              0.390000                0.800000   \n",
       "\n",
       "       Maximum_Order_Quantity  lead_time  suggested_order_quantity  \\\n",
       "0                    3.000000          2                         1   \n",
       "1                    2.500000          2                         1   \n",
       "2                    6.666667          2                         1   \n",
       "3                    2.500000          2                         1   \n",
       "4                    3.000000          2                         1   \n",
       "...                       ...        ...                       ...   \n",
       "19663                3.000000          3                         1   \n",
       "19664                2.000000          3                         1   \n",
       "19665                2.500000          3                         1   \n",
       "19666                2.000000          3                         2   \n",
       "19667                2.000000          3                         1   \n",
       "\n",
       "       actual_order_quantity  \n",
       "0                        1.0  \n",
       "1                        1.0  \n",
       "2                        2.0  \n",
       "3                        1.0  \n",
       "4                        1.0  \n",
       "...                      ...  \n",
       "19663                    1.0  \n",
       "19664                    1.0  \n",
       "19665                    1.0  \n",
       "19666                    2.0  \n",
       "19667                    1.0  \n",
       "\n",
       "[19668 rows x 20 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Backing up the existing table and performing Outlier Analysis on following columns\n",
    "store_data_Outlier = store_data\n",
    "store_data_Outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Retail Price Outlier\n",
    "(sns.boxplot(x=store_data_Outlier['retail_price']))\n",
    "#print('Count of Outliers on retail_price: ')\n",
    "len(store_data.query(\"retail_price>30\"))\n",
    "\n",
    "## Doesnt look like an Outlier, there could be Expensive products as well  -- > So , Normalizing this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='replenishment_unit_cost'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Replinishment unit cost Outlier\n",
    "sns.boxplot(x=store_data_Outlier['replenishment_unit_cost'])\n",
    "#print('Count of Outliers on replenishment_unit_cost: ')\n",
    "      #len(store_data.query(\"replenishment_unit_cost>30\"))\n",
    "\n",
    "## Doesnt look like an Outlier, there could be Expensive products as well  -- > So , Normalizing this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Ao_Forecast_Quantity'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Forecast Qty Outlier\n",
    "sns.boxplot(x=store_data_Outlier['Ao_Forecast_Quantity'])\n",
    "#print('Count of Outliers on Ao_Forecast_Quantity : ')\n",
    "      #len(store_data.query(\"replenishment_unit_cost>30\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count of Outliers on on_hand_quantity : \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Onhand\n",
    "onhand_outlier = store_data\n",
    "sns.boxplot(x=store_data_Outlier['on_hand_quantity'])\n",
    "print('Count of Outliers on on_hand_quantity : ')\n",
    "len(store_data.query(\"on_hand_quantity<-125\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Remove extreme outlier record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_data.drop(store_data[store_data['on_hand_quantity'] < -125].index, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "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>on_order_quantity</th>\n",
       "      <th>on_hand_quantity</th>\n",
       "      <th>retail_price</th>\n",
       "      <th>replenishment_unit_cost</th>\n",
       "      <th>Ao_Forecast_Quantity</th>\n",
       "      <th>suggested_order_quantity</th>\n",
       "      <th>actual_order_quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>2.60</td>\n",
       "      <td>18.56</td>\n",
       "      <td>0.531250</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>2.95</td>\n",
       "      <td>11.28</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>2.50</td>\n",
       "      <td>4.76</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>3.95</td>\n",
       "      <td>15.04</td>\n",
       "      <td>0.193750</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>2.80</td>\n",
       "      <td>4.56</td>\n",
       "      <td>0.287500</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19663</th>\n",
       "      <td>1</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.95</td>\n",
       "      <td>9.36</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19664</th>\n",
       "      <td>0</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>3.50</td>\n",
       "      <td>13.90</td>\n",
       "      <td>1.113333</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19665</th>\n",
       "      <td>2</td>\n",
       "      <td>3.625000</td>\n",
       "      <td>3.95</td>\n",
       "      <td>8.24</td>\n",
       "      <td>5.270833</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19666</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.90</td>\n",
       "      <td>7.26</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19667</th>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.80</td>\n",
       "      <td>17.60</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19668 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       on_order_quantity  on_hand_quantity  retail_price  \\\n",
       "0                      0          0.625000          2.60   \n",
       "1                      0          0.125000          2.95   \n",
       "2                      0          0.833333          2.50   \n",
       "3                      0          0.375000          3.95   \n",
       "4                      0          0.750000          2.80   \n",
       "...                  ...               ...           ...   \n",
       "19663                  1          2.000000          5.95   \n",
       "19664                  0          1.400000          3.50   \n",
       "19665                  2          3.625000          3.95   \n",
       "19666                  0          0.000000          2.90   \n",
       "19667                  0          1.000000          3.80   \n",
       "\n",
       "       replenishment_unit_cost  Ao_Forecast_Quantity  \\\n",
       "0                        18.56              0.531250   \n",
       "1                        11.28              0.243750   \n",
       "2                         4.76              0.133333   \n",
       "3                        15.04              0.193750   \n",
       "4                         4.56              0.287500   \n",
       "...                        ...                   ...   \n",
       "19663                     9.36              1.900000   \n",
       "19664                    13.90              1.113333   \n",
       "19665                     8.24              5.270833   \n",
       "19666                     7.26              0.500000   \n",
       "19667                    17.60              0.390000   \n",
       "\n",
       "       suggested_order_quantity  actual_order_quantity  \n",
       "0                             1                    1.0  \n",
       "1                             1                    1.0  \n",
       "2                             1                    2.0  \n",
       "3                             1                    1.0  \n",
       "4                             1                    1.0  \n",
       "...                         ...                    ...  \n",
       "19663                         1                    1.0  \n",
       "19664                         1                    1.0  \n",
       "19665                         1                    1.0  \n",
       "19666                         2                    2.0  \n",
       "19667                         1                    1.0  \n",
       "\n",
       "[19668 rows x 7 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping Redundant rows which may cause issue while LocalOutlierFactor \n",
    "\n",
    "store_data_Outlier= store_data_Outlier.drop(['id','order_date','order_day_of_week','store','supplier_id','product_id','category','product_case_pack',\n",
    "                        'forecast_start_date','forecast_end_date','Minimum_Order_Quantity','Maximum_Order_Quantity','lead_time'],axis=1)\n",
    "store_data_Outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['retail_price' 'replenishment_unit_cost'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-61-275d6fa42a4a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mstore_data_Outlier\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mstore_data_Outlier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'retail_price'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'replenishment_unit_cost'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   4161\u001b[0m                 \u001b[0mweight\u001b[0m  \u001b[1;36m1.0\u001b[0m     \u001b[1;36m0.8\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4162\u001b[0m         \"\"\"\n\u001b[1;32m-> 4163\u001b[1;33m         return super().drop(\n\u001b[0m\u001b[0;32m   4164\u001b[0m             \u001b[0mlabels\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4165\u001b[0m             \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   3885\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3886\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3887\u001b[1;33m                 \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3888\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3889\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[1;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[0;32m   3919\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3920\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3921\u001b[1;33m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3922\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3923\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   5280\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5281\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m\"ignore\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5282\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{labels[mask]} not found in axis\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5283\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5284\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['retail_price' 'replenishment_unit_cost'] not found in axis\""
     ]
    }
   ],
   "source": [
    "store_data_Outlier= store_data_Outlier.drop(['retail_price','replenishment_unit_cost'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='order_date', ylabel='on_hand_quantity'>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "neg_onhand = store_data.query(\"on_hand_quantity<0\")\n",
    "plt.figure(figsize=(25,15))\n",
    "#plt.subplots(ncols=1)\n",
    "sns.set_style('whitegrid')\n",
    "#sns.lineplot(data=neg_onhand, x=\"product_id\", y=\"on_hand_quantity\",\"on_order_quantity\" )\n",
    "sns.lineplot(x='order_date', y='on_hand_quantity', data=neg_onhand)\n",
    "sns.lineplot(x='order_date', y='on_order_quantity', data=neg_onhand)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### When there is -ve onhand, there is a spike in Actual order quantity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "796"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Applying LOF\n",
    "from sklearn.neighbors import LocalOutlierFactor\n",
    "lof = LocalOutlierFactor()\n",
    "yhat = lof.fit_predict(store_data_Outlier)\n",
    "store_data_Outlier1 = pd.DataFrame(store_data_Outlier)\n",
    "store_data_Outlier1 = store_data_Outlier1.assign(Outlier=[1 if val ==-1 else 0 for val in yhat])\n",
    "#store_data_Outlier1\n",
    "len(store_data_Outlier1.query(\"Outlier==1\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Outliers identified are 796 mostly due to the Forecast column hence, we are normalizing the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_data_bkp = cp.deepcopy(store_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_data = cp.deepcopy(store_data_bkp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "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>id</th>\n",
       "      <th>order_date</th>\n",
       "      <th>order_day_of_week</th>\n",
       "      <th>store</th>\n",
       "      <th>supplier_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>category</th>\n",
       "      <th>on_order_quantity</th>\n",
       "      <th>on_hand_quantity</th>\n",
       "      <th>product_case_pack</th>\n",
       "      <th>retail_price</th>\n",
       "      <th>replenishment_unit_cost</th>\n",
       "      <th>forecast_start_date</th>\n",
       "      <th>forecast_end_date</th>\n",
       "      <th>Ao_Forecast_Quantity</th>\n",
       "      <th>Minimum_Order_Quantity</th>\n",
       "      <th>Maximum_Order_Quantity</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>suggested_order_quantity</th>\n",
       "      <th>actual_order_quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>s1</td>\n",
       "      <td>50</td>\n",
       "      <td>136081</td>\n",
       "      <td>c672</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.250000</td>\n",
       "      <td>4</td>\n",
       "      <td>2.80</td>\n",
       "      <td>4.5600</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>2017-01-11</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>2017-01-05</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>s1</td>\n",
       "      <td>30</td>\n",
       "      <td>113126</td>\n",
       "      <td>c781</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.250000</td>\n",
       "      <td>4</td>\n",
       "      <td>4.95</td>\n",
       "      <td>7.3481</td>\n",
       "      <td>2017-01-05</td>\n",
       "      <td>2017-01-10</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>54</td>\n",
       "      <td>2017-01-06</td>\n",
       "      <td>Friday</td>\n",
       "      <td>s1</td>\n",
       "      <td>50</td>\n",
       "      <td>136081</td>\n",
       "      <td>c672</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.250000</td>\n",
       "      <td>4</td>\n",
       "      <td>2.80</td>\n",
       "      <td>4.5600</td>\n",
       "      <td>2017-01-06</td>\n",
       "      <td>2017-01-13</td>\n",
       "      <td>0.675000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>135</td>\n",
       "      <td>2017-01-09</td>\n",
       "      <td>Monday</td>\n",
       "      <td>s1</td>\n",
       "      <td>50</td>\n",
       "      <td>140198</td>\n",
       "      <td>c762</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>10</td>\n",
       "      <td>3.50</td>\n",
       "      <td>16.8000</td>\n",
       "      <td>2017-01-09</td>\n",
       "      <td>2017-01-15</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>176</td>\n",
       "      <td>2017-01-13</td>\n",
       "      <td>Friday</td>\n",
       "      <td>s1</td>\n",
       "      <td>50</td>\n",
       "      <td>100909</td>\n",
       "      <td>c770</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>6</td>\n",
       "      <td>2.50</td>\n",
       "      <td>5.9400</td>\n",
       "      <td>2017-01-13</td>\n",
       "      <td>2017-01-20</td>\n",
       "      <td>0.783333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18840</th>\n",
       "      <td>18841</td>\n",
       "      <td>2018-05-24</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>s2</td>\n",
       "      <td>40</td>\n",
       "      <td>157975</td>\n",
       "      <td>c891</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.458333</td>\n",
       "      <td>24</td>\n",
       "      <td>1.90</td>\n",
       "      <td>15.5200</td>\n",
       "      <td>2018-05-24</td>\n",
       "      <td>2018-06-05</td>\n",
       "      <td>0.623864</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18977</th>\n",
       "      <td>18978</td>\n",
       "      <td>2018-05-29</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>s2</td>\n",
       "      <td>50</td>\n",
       "      <td>143933</td>\n",
       "      <td>c896</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.375000</td>\n",
       "      <td>8</td>\n",
       "      <td>3.70</td>\n",
       "      <td>14.4800</td>\n",
       "      <td>2018-05-29</td>\n",
       "      <td>2018-06-04</td>\n",
       "      <td>0.662500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19249</th>\n",
       "      <td>19250</td>\n",
       "      <td>2018-06-10</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>s2</td>\n",
       "      <td>50</td>\n",
       "      <td>1059</td>\n",
       "      <td>c669</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.111111</td>\n",
       "      <td>9</td>\n",
       "      <td>1.95</td>\n",
       "      <td>6.7500</td>\n",
       "      <td>2018-06-10</td>\n",
       "      <td>2018-06-15</td>\n",
       "      <td>0.662963</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19306</th>\n",
       "      <td>19307</td>\n",
       "      <td>2018-06-14</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>s2</td>\n",
       "      <td>40</td>\n",
       "      <td>157975</td>\n",
       "      <td>c891</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>24</td>\n",
       "      <td>1.90</td>\n",
       "      <td>15.5200</td>\n",
       "      <td>2018-06-14</td>\n",
       "      <td>2018-06-26</td>\n",
       "      <td>1.240855</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19514</th>\n",
       "      <td>19515</td>\n",
       "      <td>2018-06-21</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>s2</td>\n",
       "      <td>50</td>\n",
       "      <td>100904</td>\n",
       "      <td>c770</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.125000</td>\n",
       "      <td>8</td>\n",
       "      <td>3.80</td>\n",
       "      <td>1.4500</td>\n",
       "      <td>2018-06-21</td>\n",
       "      <td>2018-06-27</td>\n",
       "      <td>0.912500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>273 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id order_date order_day_of_week store  supplier_id  product_id  \\\n",
       "18        19 2017-01-04         Wednesday    s1           50      136081   \n",
       "32        33 2017-01-05          Thursday    s1           30      113126   \n",
       "53        54 2017-01-06            Friday    s1           50      136081   \n",
       "134      135 2017-01-09            Monday    s1           50      140198   \n",
       "175      176 2017-01-13            Friday    s1           50      100909   \n",
       "...      ...        ...               ...   ...          ...         ...   \n",
       "18840  18841 2018-05-24          Thursday    s2           40      157975   \n",
       "18977  18978 2018-05-29           Tuesday    s2           50      143933   \n",
       "19249  19250 2018-06-10            Sunday    s2           50        1059   \n",
       "19306  19307 2018-06-14          Thursday    s2           40      157975   \n",
       "19514  19515 2018-06-21          Thursday    s2           50      100904   \n",
       "\n",
       "      category  on_order_quantity  on_hand_quantity  product_case_pack  \\\n",
       "18        c672                  0         -0.250000                  4   \n",
       "32        c781                  0         -0.250000                  4   \n",
       "53        c672                  1         -0.250000                  4   \n",
       "134       c762                  0         -0.200000                 10   \n",
       "175       c770                  1         -0.333333                  6   \n",
       "...        ...                ...               ...                ...   \n",
       "18840     c891                  0         -0.458333                 24   \n",
       "18977     c896                  1         -0.375000                  8   \n",
       "19249     c669                  1         -0.111111                  9   \n",
       "19306     c891                  0         -0.500000                 24   \n",
       "19514     c770                  1         -0.125000                  8   \n",
       "\n",
       "       retail_price  replenishment_unit_cost forecast_start_date  \\\n",
       "18             2.80                   4.5600          2017-01-04   \n",
       "32             4.95                   7.3481          2017-01-05   \n",
       "53             2.80                   4.5600          2017-01-06   \n",
       "134            3.50                  16.8000          2017-01-09   \n",
       "175            2.50                   5.9400          2017-01-13   \n",
       "...             ...                      ...                 ...   \n",
       "18840          1.90                  15.5200          2018-05-24   \n",
       "18977          3.70                  14.4800          2018-05-29   \n",
       "19249          1.95                   6.7500          2018-06-10   \n",
       "19306          1.90                  15.5200          2018-06-14   \n",
       "19514          3.80                   1.4500          2018-06-21   \n",
       "\n",
       "      forecast_end_date  Ao_Forecast_Quantity  Minimum_Order_Quantity  \\\n",
       "18           2017-01-11              0.750000                0.500000   \n",
       "32           2017-01-10              5.250000                1.000000   \n",
       "53           2017-01-13              0.675000                0.500000   \n",
       "134          2017-01-15              0.260000                0.000000   \n",
       "175          2017-01-20              0.783333                0.500000   \n",
       "...                 ...                   ...                     ...   \n",
       "18840        2018-06-05              0.623864                0.000000   \n",
       "18977        2018-06-04              0.662500                0.500000   \n",
       "19249        2018-06-15              0.662963                0.444444   \n",
       "19306        2018-06-26              1.240855                1.000000   \n",
       "19514        2018-06-27              0.912500                0.500000   \n",
       "\n",
       "       Maximum_Order_Quantity  lead_time  suggested_order_quantity  \\\n",
       "18                   3.000000          2                         2   \n",
       "32                   7.500000          1                         7   \n",
       "53                   3.000000          3                         1   \n",
       "134                  0.100000          3                         1   \n",
       "175                  8.333333          3                         1   \n",
       "...                       ...        ...                       ...   \n",
       "18840                0.041667          4                         1   \n",
       "18977                1.500000          2                         1   \n",
       "19249                1.333333          2                         1   \n",
       "19306                2.000000          4                         2   \n",
       "19514                3.750000          3                         1   \n",
       "\n",
       "       actual_order_quantity  \n",
       "18                       1.0  \n",
       "32                       4.0  \n",
       "53                       1.0  \n",
       "134                      1.0  \n",
       "175                      1.0  \n",
       "...                      ...  \n",
       "18840                    1.0  \n",
       "18977                    1.0  \n",
       "19249                    1.0  \n",
       "19306                    2.0  \n",
       "19514                    1.0  \n",
       "\n",
       "[273 rows x 20 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create new features\n",
    "def creatNewFeatures(data):\n",
    "  data['balance'] = data['on_hand_quantity'] + data['on_order_quantity']\n",
    "  data['year'] = data['order_date'].dt.year\n",
    "  data['month'] = data['order_date'].dt.month\n",
    "  data['day'] = data['order_date'].dt.day\n",
    "  return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "NovZmInxcqbN"
   },
   "outputs": [],
   "source": [
    "def typeconversion(data):\n",
    "\n",
    "  # change date fields to date_format_fields\n",
    "  data.order_date = pd.to_datetime(data.order_date)\n",
    "  data.forecast_start_date = pd.to_datetime(data.forecast_start_date)\n",
    "  data.forecast_end_date = pd.to_datetime(data.forecast_end_date)\n",
    "  data = data.astype({\"order_day_of_week\":'category',\"store\":'category',\"supplier_id\":'category',\"category\":'category', \"product_id\": 'category', \"year\": 'category',\"month\": 'category', \"day\": 'category'})\n",
    "  data = data.astype({\"suggested_order_quantity\": 'int', \"actual_order_quantity\": 'int', \"Minimum_Order_Quantity\": 'int', \"Maximum_Order_Quantity\":'int',\n",
    "                      \"on_order_quantity\": 'int', \"product_case_pack\": 'int', \"lead_time\": 'int'})\n",
    "  data = data.astype({\"on_hand_quantity\": 'float', \"retail_price\": 'float', \"Ao_Forecast_Quantity\": 'float',\"replenishment_unit_cost\": 'float'})\n",
    "  data.info()\n",
    "  return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler_target = MinMaxScaler(feature_range = (0, 5))\n",
    "scaler_input = MinMaxScaler(feature_range = (0, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Target Encoding for categorical features\n",
    "def encoding_features(data):\n",
    "    y = data['actual_order_quantity']\n",
    "    categorical_cols = ['order_day_of_week','store','supplier_id', 'category', 'year', 'month', 'day']\n",
    "    #Create target encoding object\n",
    "    encoder=ce.TargetEncoder(cols=categorical_cols) \n",
    "    data= encoder.fit_transform(data, y)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hp1KRX0ZNzcP"
   },
   "outputs": [],
   "source": [
    "# drop unnecessary features and createFinalDataSet\n",
    "# Onhand and OnOrde clubbed to be Balance\n",
    "def createFinalDataSet(data): \n",
    "    data = data.drop(['id', 'product_id','forecast_start_date', 'forecast_end_date', 'on_hand_quantity', 'on_order_quantity'], axis=1)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#normalize dataset\n",
    "def minMaxNormalization_input(data):\n",
    "    global scaler_input\n",
    "    for col in data.columns:\n",
    "        if col != 'actual_order_quantity':\n",
    "        # scalar_variable = MinMaxScaler(feature_range = (0,1))\n",
    "          data[[col]] = scaler_input.fit_transform(data[[col]])\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#normalize Target\n",
    "def minMaxNormalization_target(data):\n",
    "    global scaler_target\n",
    "    data[['actual_order_quantity']] = scaler_target.fit_transform(data[['actual_order_quantity']])\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_data = creatNewFeatures(store_data)\n",
    "store_data = typeconversion(store_data)\n",
    "print('store data after type conversion')\n",
    "store_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_data.to_csv('store-data-before-encoding.csv')\n",
    "store_data = encoding_features(store_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Datset after removing unnecessary feature') \n",
    "store_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_data.to_csv('final-dataset-before-normalized.csv')\n",
    "store_data = minMaxNormalization_input(store_data)\n",
    "store_data = minMaxNormalization_target(store_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_data = createFinalDataSet(store_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_data.to_csv('final-dataset-Normalized.csv')\n",
    "print('store data after normailzation')\n",
    "store_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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