time_series_modelling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fdc47df6-fc2d-4e3c-a2f8-1a118b3044c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "591a1f57-7d09-4ad7-99b3-6ad2d7298258",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('cleaned_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b75e7757-67b4-406a-b370-a9f2ce05a60a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['order_weight'] = df['order_weight'].replace(0.0, np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2ebae89a-12c3-4fb4-a18e-6de78ee7aa5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "216988    1.0\n",
       "184585    1.0\n",
       "52622     1.0\n",
       "76695     1.0\n",
       "123129    1.0\n",
       "         ... \n",
       "232879    NaN\n",
       "232955    NaN\n",
       "232962    NaN\n",
       "232978    NaN\n",
       "233004    NaN\n",
       "Name: order_weight, Length: 233016, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['order_weight'].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a3b55482-c79a-469d-a32f-456810d2d3c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='order_class_size', ylabel='order_weight'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bRt3CnDFM2H4p8NYkdwI/o3f/66qqF7RoTDPrwV0W8E8HvWbUbUiSpDHwzFUXj7qFOWOYsP07wO78y5rtq4GfzHRDkiRJ0rjY6n22+xxN744k84EF3effa9CTJEmSNBaGmdk+ATisqn4GkOT9wDXAh1s0JkmSJM11w8xsB9jYt72xq0mSJEkaYJiZ7U8C3+geagO9ZSUfn/GOJEmSpDEx7bBdVacn+TLwm/RmtH2ojSRJkrQFw8xsU1U34ENtJEmSpGkZZs22JEmSpCEYtiVJkqRGDNuSJElSI4ZtSZIkqRHDtiRJktSIYVuSJElqxLAtSZIkNWLYliRJkhoxbEuSJEmNGLYlSZKkRgzbkiRJUiOGbUmSJKkRw7YkSZLUiGFbkiRJasSwLUmSJDVi2JYkSZIaMWxLkiRJjRi2JUmSpEYM25IkSVIjhm1JkiSpkZGF7SR3JlmdZFWSya729CRXJFnTve/eN/7kJGuT3JbkiFH1LUmSJE3XqGe2X1pVB1XVkm77JOCqqpoAruq2SbI/cCxwAHAkcFaS7UfRsCRJkjRdow7bmzsKOK/7fB5wdF/9gqp6oKruANYCh2779iRJkqTpG2XYLuALSa5PsrSrPaOq1gN073t09YXAd/uOXdfVHiXJ0iSTSSY3bNjQqHVJkiRp63YY4bVfVFV3J9kDuCLJ329hbAbUatDAqloBrABYsmTJwDGSJEnStjCyme2qurt7vwf4DL1lId9PsidA935PN3wdsFff4YuAu7ddt5IkSdLwRhK2kzwlya6bPgOvAG4CVgLHd8OOBy7rPq8Ejk2yc5J9gAngum3btSRJkjScUS0jeQbwmSSbevjrqvpckm8CFyY5AfgO8DqAqro5yYXALcDDwLKq2jia1iVJkqTpGUnYrqrbgQMH1H8IvGyKY04DTmvcmiRJkjRjZtut/yRJkqSxYdiWJEmSGjFsS5IkSY2M8j7b2kbWrVvHTvf/lGeuunjUrUiSpDGw0/0bWLfuwVG3MSc4sy1JkiQ14sz2E8CiRYv4zoM78U8HvWbUrUiSpDHwzFUXs2jRHqNuY05wZluSJElqxLAtSZIkNWLYliRJkhoxbEuSJEmNGLYlSZKkRgzbkiRJUiOGbUmSJKkRw7YkSZLUiGFbkiRJasSwLUmSJDVi2JYkSZIaMWxLkiRJjRi2JUmSpEYM25IkSVIjhm1JkiSpEcO2JEmS1IhhW5IkSWrEsC1JkiQ1ssOoG9C2sdP9G3jmqotH3YakWWzHf/4JAA/Ne9pI+5A0++10/wZgj1G3MScYtp8AJiYmRt2CpDlgzZqfAjDxK/4DKmlr9jBfTJNh+wlg+fLlo25B0hywbNkyAM4888wRdyJJ48M125IkSVIjhm1JkiSpEcO2JEmS1IhhW5IkSWpkToXtJEcmuS3J2iQnjbofSZIkaUvmTNhOsj1wJvA7wP7AG5LsP9quJEmSpKnNpVv/HQqsrarbAZJcABwF3DLSrqQ56IwzzmDNmjWjbkOzzKb/JjbdAlDaZGJiwtvISo/RnJnZBhYC3+3bXtfVHiHJ0iSTSSY3bNiwzZqTpLlu3rx5zJs3b9RtSNJYmUsz2xlQq0cVqlYAKwCWLFnyqP2SfNCRJEnbylya2V4H7NW3vQi4e0S9SJIkSVs1l8L2N4GJJPsk2Qk4Flg54p4kSZKkKc2ZZSRV9XCStwOfB7YHPlFVN4+4LUmSJGlKcyZsA1TV5cDlo+5DkiRJmo65tIxEkiRJmlMM25IkSVIjhm1JkiSpEcO2JEmS1Eiqxve5L0k2AHeNug9JmkPmAz8YdROSNMc8u6oWDNox1mFbkjScJJNVtWTUfUjSuHAZiSRJktSIYVuSJElqxLAtSeq3YtQNSNI4cc22JEmS1Igz25IkSVIjhm1JkiSpEcO2JI25JPf3fX5lkjVJ9k7yniTv2sqxb01yXPsuJWk87TDqBiRJ20aSlwEfBl5RVd9JstVjquqc5o1J0hhzZluSngCSvBj4GPC7VfWPA/bvm+RzSa5P8pUk+3X1X85+J/lykvcnuS7JP3TnlCRtgWFbksbfzsBlwNFV9fdTjFkBvKOqDgHeBZw1xbgdqupQYDlw6kw3KknjxmUkkjT+HgK+DpwAnLj5ziS7AIcDF/UtLdl5inNd0r1fDyye0S4laQwZtiVp/P0CeD1wZZJ3V9V/22z/dsBPquqgaZzrge59I/4bIklb5TISSXoCqKqfA68C3pjkhM323QvckeR1AOk5cARtStLYcVZCkp4gqupHSY4Erk7yg812vxE4O8kpwI7ABcC3t3WPkjRufFy7JEmS1IjLSCRJkqRGDNuSJElSI4ZtSZIkqRHDtiRJktSIYVuSJElqxLAtSZIkNWLYlqRZLsl7kryr4fnvTDK/1fmnuOazknx6W15TkkbBsC1Js0j39MbH9Xdzkln/wLKquruqXjvqPiSpNcO2JG1jSf40yU3da3mSxUluTXIWcAOwV5L/nOS2JFcCz+07dt8kn0tyfZKvJNmvq5+b5PQkXwLeP8V1d0nyySSrk9yY5DUDxlzanfvmJEu72vbd+W/qjv2Trv7HSW7pznXBFr7vv06yqnt9K8mu3Xe+qdv/l337NyQ5tav/WZJvdud/72P985akUZr1sx+SNE6SHAK8Cfh1IMA3gL+jF6jfVFX/oRtzLHAwvb+nbwCu706xAnhrVa1J8uvAWcBvd/ueA7y8qjZOcfk/B35aVc/vetl9wJg3d491nwd8M8nFwGJgYVU9rzvuad3Yk4B9quqBvtog7wKWVdXXkuwC/L/+nVX177vzPhv4PHBuklcAE8Ch3Z/TyiS/VVVXb+E6kjTrGLYladv6TeAzVfUzgCSXAC8G7qqqa7sxL+7G/Lwbs7J73wU4HLgoyabz7dx37ou2ELQBXk4vxANQVT8eMOaPkxzTfd6LXuC9DfiVJB8G/hb4Qrf/RuBTSS4FLt3Cdb8GnJ7kU8AlVbWur3+67/Yk4CLg7VV1V5J3AK8AvtUN2aXrxbAtaU4xbEvStpUp6j/bbLsGjNkO+ElVHTTNcwy69qDz9nYmL6EXyH+jqn6e5MvAk6rqx0kOBI4AlgGvB94M/C7wW8DvAX+e5ICqenjz81bV+5L8LfBK4NokL2ez2W3gHHpB/Mq+Xv97VX10K99JkmY112xL0rZ1NXB0kicneQpwDPCVAWOOSTIvya7AqwGq6l7gjiSvg1/+mPLAIa79BeDtmzYGLCN5KvDjLmjvBxzWjZsPbFdVF9NbivLC7kece1XVl4D/CDyN3uzzoyTZt6pWV9X7gUlgv832LwN2rar39ZU/D7y5m80nycIkewzxXSVpVnBmW5K2oaq6Icm5wHVd6S+BHw8Y87+BVcBdPDKMvxE4O8kpwI7ABcC3p3n5/wqc2f0wcSPwXuCSvv2fA96a5EZ6S0c2LWtZCHyy7y4pJwPbA/8ryVPpzUJ/sKp+MsV1lyd5aXfNW4DPAnv27X8X8FCSVd32OVV1TpJfBa7plpzcD/w+cM80v6skzQqpmvL/KEqSJEl6HFxGIkmSJDXiMhJJGjNJ3gScuFn5a1W1bByvK0mzmctIJEmSpEZcRiJJkiQ1YtiWJEmSGjFsS5IkSY0YtiVJkqRG/j+VoBMEVJ+R2QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "sns.boxplot(x='order_class_size',\n",
    "           y='order_weight',\n",
    "           data=df,\n",
    "           palette='winter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6cb1e304-82a3-4bc4-9c77-e67f79a0d77d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_class_size\n",
       "Klein    375.0\n",
       "Name: order_weight, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by='order_class_size').median()['order_weight']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8a1284d8-48cd-4a12-aefe-3dc3769406b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "561.6238124015435"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the difference between mean globally and defined by order_class_size\n",
    "df['order_weight'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "05e721ea-5e17-4ce8-be3c-2a0320214222",
   "metadata": {},
   "outputs": [],
   "source": [
    "#after correcting the data with IQR, it seems that order_size_class only consist of one category. Before\n",
    "#I had more, so this code is purely to demonstrate how it is possible to calc medians per category and fill them\n",
    "#in the dataset\n",
    "def impute_order_weight(cols):\n",
    "    order_weight=cols[0]\n",
    "    order_class_size=cols[1]\n",
    "    \n",
    "    if pd.isnull(order_weight):\n",
    "        if order_class_size == 'Klein':\n",
    "            return 424\n",
    "        if order_class_size == 'Middel':\n",
    "            return 3153\n",
    "        if order_class_size == 'MiddelGroot':\n",
    "            return 4953\n",
    "        else:\n",
    "            return 7929\n",
    "    \n",
    "    else:\n",
    "        return order_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "db38fa6a-42a6-463c-82b4-690f4f5003e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_copy = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "589d7238-aef9-4f4a-be7d-6abe4885c2a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_copy['order_weight'] = df_copy[['order_weight','order_class_size']].apply(impute_order_weight, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d80b65fe-a4d4-48e9-b2b2-e9df8a8411dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "sns.heatmap(data=df_copy.isnull(),\n",
    "           yticklabels=False,\n",
    "           cbar=False,\n",
    "           cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4783fabf-6e40-4f4e-ad8c-a9a8e6ed2476",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "531.603209693755"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy['order_weight'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "021638ec-897e-44ce-a35b-d28d8612273b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#repeat above steps with main dataframe\n",
    "df['order_weight'] = df[['order_weight','order_class_size']].apply(impute_order_weight, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b9b3a97b-fafd-4abe-ad50-6cf178a1e454",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['order_weight'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "60d714fa-2fd7-43b8-82f1-272ccf0e67dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['order_load_date','order_unload_date', 'order_create_date']] = df[['order_load_date','order_unload_date', 'order_create_date']].apply(pd.to_datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6b6733e3-24cb-4514-aeae-ae1a4db18b66",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(data=df, columns=['ritsoort','order_kind', 'order_dep','order_class', 'order_class_size'], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8cccf8b4-cc60-4264-9694-283c2d372e55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(233016, 45)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e38436d2-c3de-4234-8318-98d2e6845ca6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1 = df[['order_load_date','order_size']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8d55ab11-76c5-425b-bb18-7e063efce4dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 233016 entries, 0 to 233015\n",
      "Data columns (total 2 columns):\n",
      " #   Column           Non-Null Count   Dtype         \n",
      "---  ------           --------------   -----         \n",
      " 0   order_load_date  233016 non-null  datetime64[ns]\n",
      " 1   order_size       233016 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(1)\n",
      "memory usage: 3.6 MB\n"
     ]
    }
   ],
   "source": [
    "df_1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a1696cc1-48fb-41f0-bce2-d07a1c36bf3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#transform data to timeseries\n",
    "df_1 = df_1.groupby(by='order_load_date').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "82779326-c999-46d3-97d8-550e758d3abb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-12-31', '2019-01-02', '2019-01-03', '2019-01-04',\n",
       "               '2019-01-05', '2019-01-07', '2019-01-08', '2019-01-10',\n",
       "               '2019-01-11', '2019-01-14',\n",
       "               ...\n",
       "               '2021-09-22', '2021-09-23', '2021-09-24', '2021-09-26',\n",
       "               '2021-09-27', '2021-09-28', '2021-09-29', '2021-09-30',\n",
       "               '2021-10-02', '2021-10-03'],\n",
       "              dtype='datetime64[ns]', name='order_load_date', length=780, freq=None)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "431bce83-7a1b-4030-890f-5c72a6af63bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#set timeseries frequientie to Day\n",
    "df_1 = df_1.asfreq('D', fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b3bfb6a3-875c-4d1f-be04-a8f044f2ebc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-12-31', '2019-01-01', '2019-01-02', '2019-01-03',\n",
       "               '2019-01-04', '2019-01-05', '2019-01-06', '2019-01-07',\n",
       "               '2019-01-08', '2019-01-09',\n",
       "               ...\n",
       "               '2021-09-24', '2021-09-25', '2021-09-26', '2021-09-27',\n",
       "               '2021-09-28', '2021-09-29', '2021-09-30', '2021-10-01',\n",
       "               '2021-10-02', '2021-10-03'],\n",
       "              dtype='datetime64[ns]', name='order_load_date', length=1008, freq='D')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "effbd11b-d2b7-4631-9374-46e017e42e67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='order_load_date'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "df_1['order_size'].plot(linewidth=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "98b247b5-3358-4a36-b00e-6f4bf0cf3bbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='order_load_date'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "df_1.loc['2019-06':'2019-08', 'order_size'].plot(marker='o')\n",
    "#df_1.loc['2020-06':'2020-08', 'order_size'].plot(marker='o')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "975573d1-6756-4130-8147-468af597a4b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Lag', ylabel='Autocorrelation'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x320 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4), dpi=80)\n",
    "pd.plotting.autocorrelation_plot(df_1.loc['2019-06':'2019-08', 'order_size'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "eac8af9c-0f4f-43e6-ab85-300cbfea4b33",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1['year'] = df_1.index.isocalendar().year\n",
    "df_1['week'] = df_1.index.isocalendar().week\n",
    "df_1['day'] = df_1.index.isocalendar().day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "016a0c23-6c1c-496e-b1fd-b8394d51e9d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1008 entries, 2018-12-31 to 2021-10-03\n",
      "Freq: D\n",
      "Data columns (total 4 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   order_size  1008 non-null   float64\n",
      " 1   year        1008 non-null   UInt32 \n",
      " 2   week        1008 non-null   UInt32 \n",
      " 3   day         1008 non-null   UInt32 \n",
      "dtypes: UInt32(3), float64(1)\n",
      "memory usage: 62.8 KB\n"
     ]
    }
   ],
   "source": [
    "df_1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e4f7c315-c089-48a3-9454-f3bc3d16f4ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='week', ylabel='order_size'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize=(12,4), sharex=True)\n",
    "sns.boxplot(data=df_1, x='week', y='order_size')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "cb2c6d3b-90eb-4393-892d-f038b1df7e95",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.metrics as metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8c26c583-b09b-4344-af12-eb01da9d05cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def regression_results(y_true, y_pred):\n",
    "    #regression metrics\n",
    "    explained_variance = metrics.explained_variance_score(y_true, y_pred)\n",
    "    mean_absolute_error = metrics.mean_absolute_error(y_true, y_pred)\n",
    "    mse = metrics.mean_squared_error(y_true, y_pred)\n",
    "    mean_squared_log_error = metrics.mean_squared_log_error(y_true, y_pred)\n",
    "    median_absolute_error = metrics.median_absolute_error(y_true, y_pred)\n",
    "    r2 = metrics.r2_score(y_true, y_pred)\n",
    "    \n",
    "    print('explained variance :',round(explained_variance,4))\n",
    "    print('mean squared log error :', round(mean_squared_log_error,4))\n",
    "    print('r2 :',round(r2,4))\n",
    "    print('MAE :',round(mean_absolute_error,4))\n",
    "    print('MSE :',round(mse,4))\n",
    "    print('RMSE :',round(np.sqrt(mse),4))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "c7d1f7c0-c4c2-4b38-9039-53232851ce52",
   "metadata": {},
   "outputs": [],
   "source": [
    "#add columns yesterday lag and difference with yesterday\n",
    "#df_1['yesterday'] = df_1['order_size'].shift()\n",
    "#df_1['yesterday_diff'] = df_1['yesterday'].diff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "e580110f-0f42-41d7-be53-8b13c7814bed",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop na\n",
    "#df_1 = df_1.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3899b53a-b7cf-453a-bbfd-34343b22e735",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.sort_values of                  order_size  year  week  day\n",
       "order_load_date                             \n",
       "2018-12-31         45.37500  2019     1    1\n",
       "2019-01-01          0.00000  2019     1    2\n",
       "2019-01-02        221.35570  2019     1    3\n",
       "2019-01-03        254.63450  2019     1    4\n",
       "2019-01-04        208.07718  2019     1    5>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create train en test set 50/50\n",
    "#X_train = df_1.loc[:'2019'].drop('order_size', axis=1)\n",
    "#y_train = df_1.loc[:'2019', 'order_size']\n",
    "#X_test = df_1.loc['2020'].drop('order_size', axis=1)\n",
    "#y_test = df_1.loc['2020', 'order_size']\n",
    "df_1.head().sort_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d1c4a228-3e1b-4f22-b324-4a964d444ddf",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_1.drop(['order_size'], axis=1)\n",
    "y = df_1['order_size']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3bcf85bb-712c-445d-95ca-1c3ac1818282",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "id": "92ea46de-23e2-415e-891b-aa272d5386a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "518f1ad4-0d36-4f82-8af5-bff224207a8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#scaler.fit(X_train)\n",
    "#X_train = scaler.transform(X_train)\n",
    "#X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "08f3fe57-1e03-47cb-a251-64ecd1f12d27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(705, 3)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8e312494-3108-4383-bd4c-e4f347f56721",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import TimeSeriesSplit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "012dd77f-794e-4279-9828-f02f86747c61",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.svm import SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a4df0b25-ab57-4cff-9c11-a73ef0720dbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "models = []\n",
    "models.append(('LR', LinearRegression(n_jobs=5)))\n",
    "models.append(('NN', MLPRegressor(solver='lbfgs', max_iter=1000)))\n",
    "models.append(('KNN', KNeighborsRegressor()))\n",
    "models.append(('RF', RandomForestRegressor(n_estimators=10)))\n",
    "models.append(('SVR', SVR(gamma='auto')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4cea75e7-4270-465d-a1ce-65d04d8bff96",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "53baf45e-1f87-4edd-bbd3-5194f60d5f73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: 0.158474 (0.008444)\n",
      "NN: 0.158722 (0.010170)\n",
      "KNN: 0.092664 (0.115335)\n",
      "RF: 0.054607 (0.149511)\n",
      "SVR: -0.218858 (0.085253)\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "names = []\n",
    "for name, model in models:\n",
    "    #timeseries crossvalidation\n",
    "    tscv = TimeSeriesSplit(n_splits=5)\n",
    "    \n",
    "    cv_results = cross_val_score(model, X_train, y_train, cv=tscv, scoring='r2') \n",
    "    \n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    print('%s: %f (%f)' % (name, cv_results.mean(), cv_results.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "48870dac-6474-4221-9233-92289681841a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x320 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4), dpi=80)\n",
    "plt.boxplot(results, labels=names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "46d03e1f-3fac-481b-b18b-6edf40815552",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4224d36a-35ef-4a32-971f-1188c7d315a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# performing Grid Search\n",
    "model = LinearRegression()\n",
    "#param_search = {\n",
    "#    'n_estimators': [20,50,100],\n",
    "#    'max_features': ['auto', 'sqrt', 'log2'],\n",
    "#    'max_depth':[i for i in range(5,15)]\n",
    "#}\n",
    "\n",
    "param_search = {\n",
    "    'normalize': [True]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "84e4963a-6284-4490-a66f-6db732b0ad5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create custom scorer for the model part I\n",
    "from sklearn.metrics import make_scorer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e3581d03-0295-42bf-bda5-6e5ea50e1743",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create custom scorer for the model part II\n",
    "def rmse(actual, predict):\n",
    "    predict = np.array(predict)\n",
    "    actual = np.array(actual)\n",
    "    \n",
    "    distance = predict - actual\n",
    "    \n",
    "    square_distance = distance ** 2\n",
    "    \n",
    "    mean_square_distance = square_distance.mean()\n",
    "    \n",
    "    score = np.sqrt(mean_square_distance)\n",
    "    \n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "298ab4bf-0f16-48d3-8e85-443f92f882b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create custom scorer for the model part III\n",
    "rmse_score = make_scorer(rmse, greater_is_better=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "384c8879-23b3-4bf2-93cb-bcf035886c25",
   "metadata": {},
   "outputs": [],
   "source": [
    "gsearch = GridSearchCV(estimator=model, cv=tscv, param_grid = param_search, \n",
    "                       scoring = rmse_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "3c51d25b-ffd3-4621-be3b-d21e9006b113",
   "metadata": {},
   "outputs": [],
   "source": [
    "gsearch.fit(X_train, y_train)\n",
    "\n",
    "best_score = gsearch.best_score_\n",
    "best_model = gsearch.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "fd36113c-6537-407f-83bd-b31c739c352e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-100.66632153114107"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "676f2ffe-5822-4b2e-bdf2-db988c3e80ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(normalize=True)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3ba3a208-f56e-4502-8297-15837dfdd320",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "explained variance : 0.2079\n",
      "mean squared log error : 6.7712\n",
      "r2 : 0.2069\n",
      "MAE : 85.562\n",
      "MSE : 9739.6755\n",
      "RMSE : 98.6898\n"
     ]
    }
   ],
   "source": [
    "y_true = y_test.values\n",
    "y_pred = best_model.predict(X_test)\n",
    "\n",
    "regression_results(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "8060698c-2d03-40e7-877a-8951670909d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2 = df_1.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "032f08e3-f3db-4897-95da-ad43450f02fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2['last_week'] = df_2['order_size'].shift(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "cc5dda5d-0b71-4687-afd5-8fac1270524b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2['last_week_diff'] = df_2['last_week'].diff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "15a81bef-9295-4f6b-9662-299afe565a92",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2 = df_2.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "45d31acc-85be-4351-8551-8e4a15abf462",
   "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>order_size</th>\n",
       "      <th>year</th>\n",
       "      <th>week</th>\n",
       "      <th>day</th>\n",
       "      <th>last_week</th>\n",
       "      <th>last_week_diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_load_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-08</th>\n",
       "      <td>137.8065</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-45.37500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-09</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>221.35570</td>\n",
       "      <td>221.35570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-10</th>\n",
       "      <td>206.2400</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>254.63450</td>\n",
       "      <td>33.27880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11</th>\n",
       "      <td>241.4170</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>208.07718</td>\n",
       "      <td>-46.55732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-12</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>226.09680</td>\n",
       "      <td>18.01962</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 order_size  year  week  day  last_week  last_week_diff\n",
       "order_load_date                                                        \n",
       "2019-01-08         137.8065  2019     2    2    0.00000       -45.37500\n",
       "2019-01-09           0.0000  2019     2    3  221.35570       221.35570\n",
       "2019-01-10         206.2400  2019     2    4  254.63450        33.27880\n",
       "2019-01-11         241.4170  2019     2    5  208.07718       -46.55732\n",
       "2019-01-12           0.0000  2019     2    6  226.09680        18.01962"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "3074512b-464b-4ba9-84e8-a90cd38a28eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_2 = df_2.drop(['order_size'], axis=1)\n",
    "y_2 = df_2['order_size']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "e95c5b72-cc3d-4d73-87d5-4a77e8223447",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_2, X_test, y_train_2, y_test = train_test_split(X_2, y_2, test_size=0.3, random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "45d72f56-d856-4f6b-97ea-5be828a81311",
   "metadata": {},
   "outputs": [],
   "source": [
    "# testing model on new features\n",
    "gsearch.fit(X_train_2, y_train_2)\n",
    "best_score = gsearch.best_score_\n",
    "best_model = gsearch.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "4b49d8e1-fe73-48c2-8fef-f35eb43d97e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = y_test.values\n",
    "y_pred = best_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "0f6d73cd-c4d9-478b-9e83-058506e18ed8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "explained variance : 0.2977\n",
      "mean squared log error : 6.3153\n",
      "r2 : 0.2976\n",
      "MAE : 77.5665\n",
      "MSE : 8625.0397\n",
      "RMSE : 92.8711\n"
     ]
    }
   ],
   "source": [
    "regression_results(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f73d06bb-2d6c-479e-ba30-a246188ef5d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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_size</th>\n",
       "      <th>year</th>\n",
       "      <th>week</th>\n",
       "      <th>day</th>\n",
       "      <th>last_week</th>\n",
       "      <th>last_week_diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_load_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-08</th>\n",
       "      <td>137.8065</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-45.37500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-09</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>221.35570</td>\n",
       "      <td>221.35570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-10</th>\n",
       "      <td>206.2400</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>254.63450</td>\n",
       "      <td>33.27880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11</th>\n",
       "      <td>241.4170</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>208.07718</td>\n",
       "      <td>-46.55732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-12</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>226.09680</td>\n",
       "      <td>18.01962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-29</th>\n",
       "      <td>286.0280</td>\n",
       "      <td>2021</td>\n",
       "      <td>39</td>\n",
       "      <td>3</td>\n",
       "      <td>251.38300</td>\n",
       "      <td>8.47150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>254.2270</td>\n",
       "      <td>2021</td>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>253.07300</td>\n",
       "      <td>1.69000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-01</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>2021</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>212.84250</td>\n",
       "      <td>-40.23050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-02</th>\n",
       "      <td>233.8770</td>\n",
       "      <td>2021</td>\n",
       "      <td>39</td>\n",
       "      <td>6</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-212.84250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-03</th>\n",
       "      <td>314.3106</td>\n",
       "      <td>2021</td>\n",
       "      <td>39</td>\n",
       "      <td>7</td>\n",
       "      <td>4.10000</td>\n",
       "      <td>4.10000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 order_size  year  week  day  last_week  last_week_diff\n",
       "order_load_date                                                        \n",
       "2019-01-08         137.8065  2019     2    2    0.00000       -45.37500\n",
       "2019-01-09           0.0000  2019     2    3  221.35570       221.35570\n",
       "2019-01-10         206.2400  2019     2    4  254.63450        33.27880\n",
       "2019-01-11         241.4170  2019     2    5  208.07718       -46.55732\n",
       "2019-01-12           0.0000  2019     2    6  226.09680        18.01962\n",
       "...                     ...   ...   ...  ...        ...             ...\n",
       "2021-09-29         286.0280  2021    39    3  251.38300         8.47150\n",
       "2021-09-30         254.2270  2021    39    4  253.07300         1.69000\n",
       "2021-10-01           0.0000  2021    39    5  212.84250       -40.23050\n",
       "2021-10-02         233.8770  2021    39    6    0.00000      -212.84250\n",
       "2021-10-03         314.3106  2021    39    7    4.10000         4.10000\n",
       "\n",
       "[1000 rows x 6 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "38943c1d-ae30-47ee-8655-236b4adf9b36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature: 0, Score: -2.58594\n",
      "Feature: 1, Score: -0.23106\n",
      "Feature: 2, Score: -20.95122\n",
      "Feature: 3, Score: 0.19415\n",
      "Feature: 4, Score: -0.12196\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 77,
     "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": [
    "#variable importance plot\n",
    "importance = best_model.coef_\n",
    "# summarize feature importance\n",
    "for i,v in enumerate(importance):\n",
    "\tprint('Feature: %0d, Score: %.5f' % (i,v))\n",
    "# plot feature importance\n",
    "pd.DataFrame(importance).plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "c8bf7da4-b48b-45c0-bb3a-d8fcc0183bfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -2.58594018,  -0.23106331, -20.95121536,   0.1941465 ,\n",
       "        -0.12196011])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "acb509db-35f3-4fa1-8680-04cca8a19fdd",
   "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>year</th>\n",
       "      <th>week</th>\n",
       "      <th>day</th>\n",
       "      <th>last_week</th>\n",
       "      <th>last_week_diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_load_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-03</th>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>250.8615</td>\n",
       "      <td>43.0405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-13</th>\n",
       "      <td>2019</td>\n",
       "      <td>41</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-202.5829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-14</th>\n",
       "      <td>2019</td>\n",
       "      <td>33</td>\n",
       "      <td>3</td>\n",
       "      <td>246.3090</td>\n",
       "      <td>246.3090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>38.7550</td>\n",
       "      <td>38.7550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-07</th>\n",
       "      <td>2021</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>255.2650</td>\n",
       "      <td>-33.6267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-12</th>\n",
       "      <td>2021</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>294.4575</td>\n",
       "      <td>294.2575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-28</th>\n",
       "      <td>2021</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>221.2735</td>\n",
       "      <td>-53.6225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-22</th>\n",
       "      <td>2021</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>270.3165</td>\n",
       "      <td>-8.7260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-10</th>\n",
       "      <td>2021</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>244.2860</td>\n",
       "      <td>17.6725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-11</th>\n",
       "      <td>2020</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>206.2410</td>\n",
       "      <td>-1.9520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>700 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 year  week  day  last_week  last_week_diff\n",
       "order_load_date                                            \n",
       "2020-03-03       2020    10    2   250.8615         43.0405\n",
       "2019-10-13       2019    41    7     0.0000       -202.5829\n",
       "2019-08-14       2019    33    3   246.3090        246.3090\n",
       "2020-01-06       2020     2    1    38.7550         38.7550\n",
       "2021-05-07       2021    18    5   255.2650        -33.6267\n",
       "...               ...   ...  ...        ...             ...\n",
       "2021-05-12       2021    19    3   294.4575        294.2575\n",
       "2021-05-28       2021    21    5   221.2735        -53.6225\n",
       "2021-04-22       2021    16    4   270.3165         -8.7260\n",
       "2021-03-10       2021    10    3   244.2860         17.6725\n",
       "2020-06-11       2020    24    4   206.2410         -1.9520\n",
       "\n",
       "[700 rows x 5 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "32536b99-c951-4b3d-9e45-796e168fd607",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1.to_csv('minimal_data.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a7451cf-26a7-4bec-923a-9f5562014b07",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd2e0dea-58a1-4e77-a694-3271c57d7309",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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