TimeSeries_BY_Hackathon-Rakesh.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 283,
   "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,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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "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": 175,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GpTxgtVZ5WvR",
    "outputId": "563428d0-cec2-46e8-c26a-6dcf14e57d63"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eso_dataset.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "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": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eso_dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "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",
      "id                          19668 non-null int64\n",
      "order_date                  19668 non-null object\n",
      "order_day_of_week           19668 non-null object\n",
      "store                       19668 non-null object\n",
      "supplier_id                 19668 non-null int64\n",
      "product_id                  19668 non-null int64\n",
      "category                    19668 non-null object\n",
      "on_order_quantity           19668 non-null int64\n",
      "on_hand_quantity            19668 non-null float64\n",
      "product_case_pack           19668 non-null int64\n",
      "retail_price                19668 non-null float64\n",
      "replenishment_unit_cost     19668 non-null float64\n",
      "forecast_start_date         19668 non-null object\n",
      "forecast_end_date           19668 non-null object\n",
      "Ao_Forecast_Quantity        19668 non-null float64\n",
      "Minimum_Order_Quantity      19668 non-null int64\n",
      "Maximum_Order_Quantity      19668 non-null int64\n",
      "lead_time                   19668 non-null int64\n",
      "suggested_order_quantity    19668 non-null int64\n",
      "actual_order_quantity       19668 non-null float64\n",
      "dtypes: float64(5), int64(9), object(6)\n",
      "memory usage: 2.6+ MB\n"
     ]
    }
   ],
   "source": [
    "eso_dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XyQeroS_A6T0",
    "outputId": "8dd80d8c-3480-4fc3-86e7-fd3f4273aa13"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "china: ['Monday' 'Wednesday' 'Thursday' 'Friday' 'Saturday' 'Tuesday' 'Sunday']\n",
      "['s1' 's2']\n"
     ]
    }
   ],
   "source": [
    "print('china:', eso_dataset.order_day_of_week.unique())\n",
    "print(eso_dataset.store.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mD-PGrILNXzU",
    "outputId": "ca83b8b3-b5d1-48e3-f5ce-f4269110b6a3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    s1\n",
       "1    s1\n",
       "2    s1\n",
       "3    s1\n",
       "4    s1\n",
       "Name: store, dtype: object"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eso_dataset.store[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "id": "NMwXHDSxfrlI"
   },
   "outputs": [],
   "source": [
    "store_data = cp.deepcopy(eso_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C6gUwuqZfSop"
   },
   "source": [
    "###Set the date fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "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",
       "\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": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_data[['order_date', 'forecast_start_date', 'forecast_end_date']][0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "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": 183,
   "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": 183,
     "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": 184,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "k2WjEgUthxLS",
    "outputId": "e2133168-ea28-4834-e4d5-30c1ac41c73c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 184,
     "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": "markdown",
   "metadata": {
    "id": "g0A17f7ULZel"
   },
   "source": [
    "##Inspect category variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fiPl-CaYqTyJ",
    "outputId": "eb753256-acb8-4aab-c874-4a84de823043"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "order_day_of_week :    ['Monday' 'Wednesday' 'Thursday' 'Friday' 'Saturday' 'Tuesday' 'Sunday']\n",
      "store :    ['s1' 's2']\n",
      "supplier_id :    [50 30 40 90 10 70 20 60]\n",
      "category :    ['c654' 'c896' 'c762' 'c672' 'c773' 'c2052' 'c760' 'c786' 'c767' 'c770'\n",
      " 'c771' 'c790' 'c781' 'c768' 'c793' 'c794' 'c791' 'c595' 'c901' 'c893'\n",
      " 'c895' 'c902' 'c906' 'c646' 'c608' 'c900' 'c894' 'c1845' 'c1843' 'c658'\n",
      " 'c650' 'c596' 'c649' 'c884' 'c662' 'c1844' 'c885' 'c636' 'c661' 'c597'\n",
      " 'c602' 'c644' 'c891' 'c599' 'c656' 'c647' 'c605' 'c657' 'c607' 'c1037'\n",
      " 'c801' 'c886' 'c766' 'c653' 'c645' 'c888' 'c1850' 'c603' 'c655' 'c889'\n",
      " 'c601' 'c800' 'c904' 'c803' 'c805' 'c1101' 'c2056' 'c1102' 'c1045' 'c598'\n",
      " 'c802' 'c1851' 'c761' 'c621' 'c785' 'c676' 'c986' 'c1106' 'c991' 'c609'\n",
      " 'c1050' 'c1847' 'c985' 'c990' 'c637' 'c1094' 'c1096' 'c981' 'c1110'\n",
      " 'c1026' 'c1027' 'c1052' 'c1053' 'c1217' 'c628' 'c764' 'c1552' 'c1566'\n",
      " 'c1551' 'c1028' 'c1049' 'c1047' 'c1973' 'c1038' 'c669' 'c780' 'c626']\n",
      "\n",
      " other variable is product_id, not printed here as the list is big\n",
      "total number of unique products: 950\n"
     ]
    }
   ],
   "source": [
    "\n",
    "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": "code",
   "execution_count": 186,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 668
    },
    "id": "8Gmi2zMVmFX3",
    "outputId": "207b099c-2c25-4682-9050-32c611a924d4"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 187,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 448
    },
    "id": "ioHv6eHgnPoZ",
    "outputId": "5366f403-43d2-479f-9660-76aaa92c50eb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f968570>"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.figsize=(25,25)\n",
    "# plt.plot(store_data['order_date'],store_data['suggested_order_quantity'],  marker='o')\n",
    "# plt.plot(store_data['order_date'],store_data['actual_order_quantity'], marker='')\n",
    "\n",
    "# plt.show()\n",
    "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\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 447
    },
    "id": "5aDVn8CggNzh",
    "outputId": "4969ef7e-3cc8-4887-e2ee-e981b8623903"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x5adc3b0>"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": "markdown",
   "metadata": {
    "id": "iCZgZG1tF873"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "id": "hp1KRX0ZNzcP"
   },
   "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": 190,
   "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": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_data = creatNewFeatures(store_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "id": "RaKzVZH7ZtpN"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19668 entries, 0 to 19667\n",
      "Data columns (total 24 columns):\n",
      "id                          19668 non-null int64\n",
      "order_date                  19668 non-null datetime64[ns]\n",
      "order_day_of_week           19668 non-null category\n",
      "store                       19668 non-null category\n",
      "supplier_id                 19668 non-null category\n",
      "product_id                  19668 non-null category\n",
      "category                    19668 non-null category\n",
      "on_order_quantity           19668 non-null int32\n",
      "on_hand_quantity            19668 non-null float64\n",
      "product_case_pack           19668 non-null int32\n",
      "retail_price                19668 non-null float64\n",
      "replenishment_unit_cost     19668 non-null float64\n",
      "forecast_start_date         19668 non-null datetime64[ns]\n",
      "forecast_end_date           19668 non-null datetime64[ns]\n",
      "Ao_Forecast_Quantity        19668 non-null float64\n",
      "Minimum_Order_Quantity      19668 non-null int32\n",
      "Maximum_Order_Quantity      19668 non-null int32\n",
      "lead_time                   19668 non-null int32\n",
      "suggested_order_quantity    19668 non-null int32\n",
      "actual_order_quantity       19668 non-null int32\n",
      "balance                     19668 non-null float64\n",
      "year                        19668 non-null category\n",
      "month                       19668 non-null category\n",
      "day                         19668 non-null category\n",
      "dtypes: category(8), datetime64[ns](3), float64(5), int32(7), int64(1)\n",
      "memory usage: 2.1 MB\n"
     ]
    }
   ],
   "source": [
    "  store_data = typeconversion(store_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 222
    },
    "id": "5-2uRiplqnVl",
    "outputId": "6cc31396-a61d-4fc8-fd83-3b397cb3e56d"
   },
   "outputs": [
    {
     "data": {
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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",
       "      <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>...</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",
       "      <th>balance</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2017-01-02</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>...</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</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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>...</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</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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>...</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</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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>...</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</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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>...</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</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "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  ...  \\\n",
       "0                  0          0.625000                 16  ...   \n",
       "1                  0          0.125000                  8  ...   \n",
       "2                  0          0.833333                  6  ...   \n",
       "3                  0          0.375000                  8  ...   \n",
       "4                  0          0.750000                  4  ...   \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   balance  year  \\\n",
       "0         2                         1                      1  0.625000  2017   \n",
       "1         2                         1                      1  0.125000  2017   \n",
       "2         2                         1                      2  0.833333  2017   \n",
       "3         2                         1                      1  0.375000  2017   \n",
       "4         2                         1                      1  0.750000  2017   \n",
       "\n",
       "   month  day  \n",
       "0      1    2  \n",
       "1      1    2  \n",
       "2      1    2  \n",
       "3      1    2  \n",
       "4      1    2  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "id": "f3b-NEvMRlcv"
   },
   "outputs": [],
   "source": [
    "store_data_backup = cp.deepcopy(store_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_data= cp.deepcopy(store_data_backup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "id": "8XMwUahJLgXr"
   },
   "outputs": [],
   "source": [
    "# # Encoding for categorical features\n",
    "# def encoding_features(data):\n",
    "#     categorical_cols = ['order_day_of_week','store','supplier_id', 'category']\n",
    "#     #     oneHotEncoder.fit_transform(data['order_day_of_week','store','supplier_id', 'category']\n",
    "#     oneHotEncoder = OneHotEncoder(sparse = False)\n",
    "#     column_transform = make_column_transformer(\n",
    "#         (oneHotEncoder, categorical_cols))\n",
    "\n",
    "#     array_hot_encoded=column_transform.fit_transform(data)\n",
    "#     #Convert it to df\n",
    "#     data_hot_encoded = pd.DataFrame(array_hot_encoded, index=data.index)\n",
    "#     return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Encoding for categorical features\n",
    "# def encoding_features(data):\n",
    "#     categorical_cols = ['order_day_of_week','store','supplier_id']\n",
    "    \n",
    "#     oneHotEncoder = OneHotEncoder(sparse = False)\n",
    "#     transformed_data = oneHotEncoder.fit_transform(data[categorical_cols])\n",
    "#     print(transformed_data[0:5])\n",
    "#     # the above transformed_data is an array so convert it to dataframe\n",
    "#     encoded_data = pd.DataFrame(transformed_data, index=data.index)\n",
    "#     encoded_data.head(5)\n",
    "#     # now concatenate the original data and the encoded data using pandas\n",
    "#     concatenated_data = pd.concat([data, encoded_data], axis=1)\n",
    "    \n",
    "#     return encoded_data,concatenated_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "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",
    "    for col in categorical_cols:\n",
    "        encoder=ce.TargetEncoder(cols=col) \n",
    "        data[col] = encoder.fit_transform(data[col], y)\n",
    "    return data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop unnecessary features and createFinalDataSet\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": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "#normalize dataset\n",
    "def minMaxNormalization(data):\n",
    "    \n",
    "    for col in data.columns:\n",
    "        scalar_variable = MinMaxScaler(feature_range = (0,1))\n",
    "        data[[col]] = scalar_variable.fit_transform(data[[col]])\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "# store_data = creatNewFeatures(store_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>product_case_pack</th>\n",
       "      <th>...</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",
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       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    <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",
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       "      <td>c896</td>\n",
       "      <td>0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.125000</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.833333</td>\n",
       "      <td>6</td>\n",
       "      <td>...</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</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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>...</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</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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>...</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</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "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  ...  \\\n",
       "0                  0          0.625000                 16  ...   \n",
       "1                  0          0.125000                  8  ...   \n",
       "2                  0          0.833333                  6  ...   \n",
       "3                  0          0.375000                  8  ...   \n",
       "4                  0          0.750000                  4  ...   \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   balance  year  \\\n",
       "0         2                         1                      1  0.625000  2017   \n",
       "1         2                         1                      1  0.125000  2017   \n",
       "2         2                         1                      2  0.833333  2017   \n",
       "3         2                         1                      1  0.375000  2017   \n",
       "4         2                         1                      1  0.750000  2017   \n",
       "\n",
       "   month  day  \n",
       "0      1    2  \n",
       "1      1    2  \n",
       "2      1    2  \n",
       "3      1    2  \n",
       "4      1    2  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_data.to_csv('store-data-before-encoding.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_data = encoding_features(store_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_data = createFinalDataSet(store_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_date</th>\n",
       "      <th>order_day_of_week</th>\n",
       "      <th>store</th>\n",
       "      <th>supplier_id</th>\n",
       "      <th>category</th>\n",
       "      <th>product_case_pack</th>\n",
       "      <th>retail_price</th>\n",
       "      <th>replenishment_unit_cost</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",
       "      <th>balance</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>1.699686</td>\n",
       "      <td>1.78448</td>\n",
       "      <td>1.573396</td>\n",
       "      <td>1.050360</td>\n",
       "      <td>16</td>\n",
       "      <td>2.60</td>\n",
       "      <td>18.56</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</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>1.842885</td>\n",
       "      <td>1.471576</td>\n",
       "      <td>1.542857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>1.699686</td>\n",
       "      <td>1.78448</td>\n",
       "      <td>1.573396</td>\n",
       "      <td>1.103970</td>\n",
       "      <td>8</td>\n",
       "      <td>2.95</td>\n",
       "      <td>11.28</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</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>1.842885</td>\n",
       "      <td>1.471576</td>\n",
       "      <td>1.542857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>1.699686</td>\n",
       "      <td>1.78448</td>\n",
       "      <td>1.573396</td>\n",
       "      <td>1.103970</td>\n",
       "      <td>6</td>\n",
       "      <td>2.50</td>\n",
       "      <td>4.76</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</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.842885</td>\n",
       "      <td>1.471576</td>\n",
       "      <td>1.542857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>1.699686</td>\n",
       "      <td>1.78448</td>\n",
       "      <td>1.573396</td>\n",
       "      <td>1.262023</td>\n",
       "      <td>8</td>\n",
       "      <td>3.95</td>\n",
       "      <td>15.04</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</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>1.842885</td>\n",
       "      <td>1.471576</td>\n",
       "      <td>1.542857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>1.699686</td>\n",
       "      <td>1.78448</td>\n",
       "      <td>1.573396</td>\n",
       "      <td>1.103070</td>\n",
       "      <td>4</td>\n",
       "      <td>2.80</td>\n",
       "      <td>4.56</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</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.842885</td>\n",
       "      <td>1.471576</td>\n",
       "      <td>1.542857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  order_date  order_day_of_week    store  supplier_id  category  \\\n",
       "0 2017-01-02           1.699686  1.78448     1.573396  1.050360   \n",
       "1 2017-01-02           1.699686  1.78448     1.573396  1.103970   \n",
       "2 2017-01-02           1.699686  1.78448     1.573396  1.103970   \n",
       "3 2017-01-02           1.699686  1.78448     1.573396  1.262023   \n",
       "4 2017-01-02           1.699686  1.78448     1.573396  1.103070   \n",
       "\n",
       "   product_case_pack  retail_price  replenishment_unit_cost  \\\n",
       "0                 16          2.60                    18.56   \n",
       "1                  8          2.95                    11.28   \n",
       "2                  6          2.50                     4.76   \n",
       "3                  8          3.95                    15.04   \n",
       "4                  4          2.80                     4.56   \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   balance  \\\n",
       "0          2                         1                      1  0.625000   \n",
       "1          2                         1                      1  0.125000   \n",
       "2          2                         1                      2  0.833333   \n",
       "3          2                         1                      1  0.375000   \n",
       "4          2                         1                      1  0.750000   \n",
       "\n",
       "       year     month       day  \n",
       "0  1.842885  1.471576  1.542857  \n",
       "1  1.842885  1.471576  1.542857  \n",
       "2  1.842885  1.471576  1.542857  \n",
       "3  1.842885  1.471576  1.542857  \n",
       "4  1.842885  1.471576  1.542857  "
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_data.to_csv('final-dataset-before-normalized.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_data = minMaxNormalization(store_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 414
    },
    "id": "YcDRbR9clf90",
    "outputId": "6a6c8ef4-413d-47bd-884b-ba335938bf10"
   },
   "outputs": [],
   "source": [
    "# store_data_dates = pd.DataFrame()\n",
    "# store_data_dates['forecast_duration'] = store_data['forecast_end_date'] - store_data['forecast_start_date']\n",
    "# # store_data_dates['forecast_duration'].unique()\n",
    "# # pd.unique(pd.Series([store_data_dates['forecast_duration']]))\n",
    "\n",
    "# store_data_dates.query(\"forecast_duration != '6'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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>order_day_of_week</th>\n",
       "      <th>store</th>\n",
       "      <th>supplier_id</th>\n",
       "      <th>category</th>\n",
       "      <th>product_case_pack</th>\n",
       "      <th>retail_price</th>\n",
       "      <th>replenishment_unit_cost</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",
       "      <th>balance</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.541528</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.114664</td>\n",
       "      <td>0.003360</td>\n",
       "      <td>0.062762</td>\n",
       "      <td>0.023490</td>\n",
       "      <td>0.124530</td>\n",
       "      <td>0.003700</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.718999</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.541528</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.114664</td>\n",
       "      <td>0.006937</td>\n",
       "      <td>0.029289</td>\n",
       "      <td>0.027405</td>\n",
       "      <td>0.075684</td>\n",
       "      <td>0.000849</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.023780</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.716342</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.541528</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.114664</td>\n",
       "      <td>0.006937</td>\n",
       "      <td>0.020921</td>\n",
       "      <td>0.022371</td>\n",
       "      <td>0.031938</td>\n",
       "      <td>0.000348</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.048811</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.720106</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.541528</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.114664</td>\n",
       "      <td>0.017483</td>\n",
       "      <td>0.029289</td>\n",
       "      <td>0.038591</td>\n",
       "      <td>0.100913</td>\n",
       "      <td>0.000675</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.023780</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.717671</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.541528</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.114664</td>\n",
       "      <td>0.006877</td>\n",
       "      <td>0.012552</td>\n",
       "      <td>0.025727</td>\n",
       "      <td>0.030596</td>\n",
       "      <td>0.000501</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.013767</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.719663</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013776</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_date  order_day_of_week  store  supplier_id  category  \\\n",
       "0         0.0           0.541528    1.0     0.114664  0.003360   \n",
       "1         0.0           0.541528    1.0     0.114664  0.006937   \n",
       "2         0.0           0.541528    1.0     0.114664  0.006937   \n",
       "3         0.0           0.541528    1.0     0.114664  0.017483   \n",
       "4         0.0           0.541528    1.0     0.114664  0.006877   \n",
       "\n",
       "   product_case_pack  retail_price  replenishment_unit_cost  \\\n",
       "0           0.062762      0.023490                 0.124530   \n",
       "1           0.029289      0.027405                 0.075684   \n",
       "2           0.020921      0.022371                 0.031938   \n",
       "3           0.029289      0.038591                 0.100913   \n",
       "4           0.012552      0.025727                 0.030596   \n",
       "\n",
       "   Ao_Forecast_Quantity  Minimum_Order_Quantity  Maximum_Order_Quantity  \\\n",
       "0              0.003700                   0.010                0.058824   \n",
       "1              0.000849                   0.010                0.023780   \n",
       "2              0.000348                   0.025                0.048811   \n",
       "3              0.000675                   0.010                0.023780   \n",
       "4              0.000501                   0.010                0.013767   \n",
       "\n",
       "   lead_time  suggested_order_quantity  actual_order_quantity   balance  year  \\\n",
       "0   0.181818                       0.0                  0.000  0.718999   1.0   \n",
       "1   0.181818                       0.0                  0.000  0.716342   1.0   \n",
       "2   0.181818                       0.0                  0.025  0.720106   1.0   \n",
       "3   0.181818                       0.0                  0.000  0.717671   1.0   \n",
       "4   0.181818                       0.0                  0.000  0.719663   1.0   \n",
       "\n",
       "   month       day  \n",
       "0    0.0  0.013776  \n",
       "1    0.0  0.013776  \n",
       "2    0.0  0.013776  \n",
       "3    0.0  0.013776  \n",
       "4    0.0  0.013776  "
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_data.to_csv('final-dataset-Normalized.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = store_data.drop('actual_order_quantity',axis=1)\n",
    "y = store_data['actual_order_quantity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X1 = X.to_numpy()\n",
    "# y1 = y.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size =0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of LR 0.6154735861396071\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lm = LinearRegression()\n",
    "lm.fit(X_train, y_train)\n",
    "y_predict = lm.predict(X_test)\n",
    "print('Accuracy of LR', lm.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Index' object has no attribute 'loc'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-292-e4c51e864a34>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;31m# plot feature importance\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mval\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimportance\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[1;32m----> 4\u001b[1;33m     \u001b[0mimportances\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstore_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mval\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      5\u001b[0m     \u001b[0mimportances\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'barh'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcmap\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;31m# pyplot.bar([x for x in range(len(importance))], importance)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Index' object has no attribute 'loc'"
     ]
    }
   ],
   "source": [
    "# importance= lm.coef_\n",
    "# # plot feature importance\n",
    "# for val, index in enumerate(importance): \n",
    "#     importances=pd.Series(store_data.columns.loc(val), index=val)\n",
    "#     importances.plot(kind='barh', figsize=(12,8), cmap=cmap)\n",
    "# # pyplot.bar([x for x in range(len(importance))], importance)\n",
    "# # pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size =0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size =0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "name": "TimeSeries-BY-Hackathon",
   "provenance": []
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  "kernelspec": {
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   "language": "python",
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  "language_info": {
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