notebooks/mainnotebook2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "716480c9-fdde-4f2f-9353-e4d64e0bc44e",
   "metadata": {},
   "source": [
    "# Dominoes Project : Predictive Purchase Order System\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "699a7555-8be3-47be-81fa-7aba4332c84e",
   "metadata": {},
   "source": [
    "# Part 2 : Model Building and Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "41005447-ed34-488f-8322-1c412067acb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "604edcfd-d9fc-4999-a626-3068053da64d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pizza_name_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>order_date</th>\n",
       "      <th>order_time</th>\n",
       "      <th>unit_price</th>\n",
       "      <th>total_price</th>\n",
       "      <th>pizza_size</th>\n",
       "      <th>pizza_category</th>\n",
       "      <th>pizza_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>hawaiian_m</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>11:38:36</td>\n",
       "      <td>13.25</td>\n",
       "      <td>13.25</td>\n",
       "      <td>M</td>\n",
       "      <td>Classic</td>\n",
       "      <td>The Hawaiian Pizza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>classic_dlx_m</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>11:57:40</td>\n",
       "      <td>16.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>M</td>\n",
       "      <td>Classic</td>\n",
       "      <td>The Classic Deluxe Pizza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>five_cheese_l</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>11:57:40</td>\n",
       "      <td>18.50</td>\n",
       "      <td>18.50</td>\n",
       "      <td>L</td>\n",
       "      <td>Veggie</td>\n",
       "      <td>The Five Cheese Pizza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ital_supr_l</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>11:57:40</td>\n",
       "      <td>20.75</td>\n",
       "      <td>20.75</td>\n",
       "      <td>L</td>\n",
       "      <td>Supreme</td>\n",
       "      <td>The Italian Supreme Pizza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mexicana_m</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>11:57:40</td>\n",
       "      <td>16.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>M</td>\n",
       "      <td>Veggie</td>\n",
       "      <td>The Mexicana Pizza</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pizza_name_id  quantity order_date order_time  unit_price  total_price  \\\n",
       "0     hawaiian_m         1 2015-01-01   11:38:36       13.25        13.25   \n",
       "1  classic_dlx_m         1 2015-01-01   11:57:40       16.00        16.00   \n",
       "2  five_cheese_l         1 2015-01-01   11:57:40       18.50        18.50   \n",
       "3    ital_supr_l         1 2015-01-01   11:57:40       20.75        20.75   \n",
       "4     mexicana_m         1 2015-01-01   11:57:40       16.00        16.00   \n",
       "\n",
       "  pizza_size pizza_category                 pizza_name  \n",
       "0          M        Classic         The Hawaiian Pizza  \n",
       "1          M        Classic   The Classic Deluxe Pizza  \n",
       "2          L         Veggie      The Five Cheese Pizza  \n",
       "3          L        Supreme  The Italian Supreme Pizza  \n",
       "4          M         Veggie         The Mexicana Pizza  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs = pd.read_excel('Pizza_Sale_Cleaned.xlsx')\n",
    "dfs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e99ec94e-153a-4191-8889-3745db0e67f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs['pizza_name_id'] = dfs['pizza_name_id'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8007a75b-8fd1-444f-b75e-c5ca9b146950",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2d141aa7-fbc1-4f1e-be83-ead6b4e6bb94",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_date</th>\n",
       "      <th>pizza_name_id</th>\n",
       "      <th>quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>bbq_ckn_l</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>bbq_ckn_m</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>bbq_ckn_s</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>big_meat_s</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>calabrese_m</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>22959</th>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>the_greek_l</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22960</th>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>the_greek_xl</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22961</th>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>veggie_veg_l</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22962</th>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>veggie_veg_m</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22963</th>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>veggie_veg_s</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22964 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      order_date pizza_name_id  quantity\n",
       "0     2015-01-01     bbq_ckn_l         6\n",
       "1     2015-01-01     bbq_ckn_m         4\n",
       "2     2015-01-01     bbq_ckn_s         1\n",
       "3     2015-01-01    big_meat_s         5\n",
       "4     2015-01-01   calabrese_m         1\n",
       "...          ...           ...       ...\n",
       "22959 2015-12-31   the_greek_l         1\n",
       "22960 2015-12-31  the_greek_xl         1\n",
       "22961 2015-12-31  veggie_veg_l         4\n",
       "22962 2015-12-31  veggie_veg_m         2\n",
       "22963 2015-12-31  veggie_veg_s         4\n",
       "\n",
       "[22964 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs['order_date'] = pd.to_datetime(dfs['order_date'])\n",
    "df_agg = dfs.groupby(['order_date', 'pizza_name_id']).agg({'quantity': 'sum',}).reset_index()\n",
    "df_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "72e659fe-94f9-44ef-9ea0-a27b3aed66aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_date       0\n",
       "pizza_name_id    0\n",
       "quantity         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d25ea06e-a5f9-4b2b-be77-06ad8b9d04df",
   "metadata": {},
   "source": [
    "## Outlier Removal\n",
    "#### Try with and without\n",
    "#### First let's remove it and store in a variable to see "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d4ceb0a3-9b81-4ec1-858a-d2192e57a9d1",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>order_date</th>\n",
       "      <th>quantity</th>\n",
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       "      <td>22964</td>\n",
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       "      <th>mean</th>\n",
       "      <td>2015-06-30 01:39:04.608953088</td>\n",
       "      <td>2.158770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2015-01-01 00:00:00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2015-03-31 00:00:00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2015-06-30 00:00:00</td>\n",
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       "      <th>75%</th>\n",
       "      <td>2015-09-30 00:00:00</td>\n",
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       "      <td>2015-12-31 00:00:00</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.436352</td>\n",
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      ],
      "text/plain": [
       "                          order_date      quantity\n",
       "count                          22964  22964.000000\n",
       "mean   2015-06-30 01:39:04.608953088      2.158770\n",
       "min              2015-01-01 00:00:00      1.000000\n",
       "25%              2015-03-31 00:00:00      1.000000\n",
       "50%              2015-06-30 00:00:00      2.000000\n",
       "75%              2015-09-30 00:00:00      3.000000\n",
       "max              2015-12-31 00:00:00     15.000000\n",
       "std                              NaN      1.436352"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "867c25ac-5b6d-4706-bcdb-91f7d227edc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DataFrame shape: (22964, 3)\n",
      "Outliers DataFrame shape: (359, 3)\n",
      "Filtered DataFrame shape: (22605, 3)\n"
     ]
    }
   ],
   "source": [
    "# Calculate Z-scores for the 'quantity' column\n",
    "df_agg['z_score'] = (df_agg['quantity'] - df_agg['quantity'].mean()) / df_agg['quantity'].std()\n",
    "\n",
    "# Filter out outliers based on Z-score threshold of 3\n",
    "threshold = 3\n",
    "df_filtered = df_agg[np.abs(df_agg['z_score']) <= threshold]\n",
    "df_outliers = df_agg[np.abs(df_agg['z_score']) > threshold]\n",
    "\n",
    "# Drop the z_score column as it's no longer needed\n",
    "df_filtered = df_filtered.drop(columns=['z_score'])\n",
    "df_outliers = df_outliers.drop(columns=['z_score'])\n",
    "df_agg = df_agg.drop(columns=['z_score'])\n",
    "\n",
    "print(f\"Original DataFrame shape: {df_agg.shape}\")\n",
    "print(f\"Outliers DataFrame shape: {df_outliers.shape}\")\n",
    "print(f\"Filtered DataFrame shape: {df_filtered.shape}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "14e2a436-8d76-4102-b27b-9e9131638cc2",
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   "outputs": [
    {
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       "      <td>7.908078</td>\n",
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       "      <td>1.311708</td>\n",
       "    </tr>\n",
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       "      <th>min</th>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
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       "      <th>50%</th>\n",
       "      <td>7.000000</td>\n",
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       "      <th>75%</th>\n",
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      "text/plain": [
       "         quantity\n",
       "count  359.000000\n",
       "mean     7.908078\n",
       "std      1.311708\n",
       "min      7.000000\n",
       "25%      7.000000\n",
       "50%      7.000000\n",
       "75%      8.000000\n",
       "max     15.000000"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_outliers.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "d458a18a-556e-421e-bfe2-d26736e0d932",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quantity</th>\n",
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       "      <td>2.067463</td>\n",
       "    </tr>\n",
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       "      <th>std</th>\n",
       "      <td>1.239076</td>\n",
       "    </tr>\n",
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       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
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       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
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       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
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       "      <th>75%</th>\n",
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      "text/plain": [
       "           quantity\n",
       "count  22605.000000\n",
       "mean       2.067463\n",
       "std        1.239076\n",
       "min        1.000000\n",
       "25%        1.000000\n",
       "50%        2.000000\n",
       "75%        3.000000\n",
       "max        6.000000"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "88296484-9de7-4713-8b92-c180c539eabb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2839"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_outliers['quantity'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "aebd359a-08d3-4347-ab7e-c9ba0d15728b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_date</th>\n",
       "      <th>pizza_name_id</th>\n",
       "      <th>quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>bbq_ckn_l</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  order_date pizza_name_id  quantity\n",
       "0 2015-01-01     bbq_ckn_l         6"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "99ee2b40-4ebe-4141-b9cb-a62674ddcaf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extra_features_from_date(df):\n",
    "    df['day_of_week'] = df['order_date'].dt.weekday +1 #Starts with 1 as Monday to 7 as Sunday\n",
    "    df['day_of_year'] = df['order_date'].dt.dayofyear \n",
    "    df['day_of_month'] = df['order_date'].dt.day\n",
    "    df['week_of_year'] = df['order_date'].dt.strftime('%W') #week starts on Monday, new year days preceeding the first monday is week 0\n",
    "    df['month'] = df['order_date'].dt.month    \n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d2932273-0c32-41fd-8add-b049acd06a07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_date</th>\n",
       "      <th>pizza_size</th>\n",
       "      <th>pizza_name</th>\n",
       "      <th>unit_price</th>\n",
       "      <th>quantity</th>\n",
       "      <th>pizza_name_id</th>\n",
       "      <th>pizza_category</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>week_of_year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>L</td>\n",
       "      <td>The Barbecue Chicken Pizza</td>\n",
       "      <td>20.75</td>\n",
       "      <td>6</td>\n",
       "      <td>bbq_ckn_l</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>L</td>\n",
       "      <td>The California Chicken Pizza</td>\n",
       "      <td>20.75</td>\n",
       "      <td>2</td>\n",
       "      <td>cali_ckn_l</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>L</td>\n",
       "      <td>The Chicken Pesto Pizza</td>\n",
       "      <td>20.75</td>\n",
       "      <td>3</td>\n",
       "      <td>ckn_pesto_l</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>L</td>\n",
       "      <td>The Classic Deluxe Pizza</td>\n",
       "      <td>20.50</td>\n",
       "      <td>1</td>\n",
       "      <td>classic_dlx_l</td>\n",
       "      <td>Classic</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>L</td>\n",
       "      <td>The Four Cheese Pizza</td>\n",
       "      <td>17.95</td>\n",
       "      <td>3</td>\n",
       "      <td>four_cheese_l</td>\n",
       "      <td>Veggie</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22744</th>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>S</td>\n",
       "      <td>The Spicy Italian Pizza</td>\n",
       "      <td>12.50</td>\n",
       "      <td>3</td>\n",
       "      <td>spicy_ital_s</td>\n",
       "      <td>Supreme</td>\n",
       "      <td>4</td>\n",
       "      <td>365</td>\n",
       "      <td>31</td>\n",
       "      <td>52</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22745</th>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>S</td>\n",
       "      <td>The Spinach Pesto Pizza</td>\n",
       "      <td>12.50</td>\n",
       "      <td>2</td>\n",
       "      <td>spin_pesto_s</td>\n",
       "      <td>Veggie</td>\n",
       "      <td>4</td>\n",
       "      <td>365</td>\n",
       "      <td>31</td>\n",
       "      <td>52</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22746</th>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>S</td>\n",
       "      <td>The Spinach and Feta Pizza</td>\n",
       "      <td>12.00</td>\n",
       "      <td>3</td>\n",
       "      <td>spinach_fet_s</td>\n",
       "      <td>Veggie</td>\n",
       "      <td>4</td>\n",
       "      <td>365</td>\n",
       "      <td>31</td>\n",
       "      <td>52</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22747</th>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>S</td>\n",
       "      <td>The Vegetables + Vegetables Pizza</td>\n",
       "      <td>12.00</td>\n",
       "      <td>4</td>\n",
       "      <td>veggie_veg_s</td>\n",
       "      <td>Veggie</td>\n",
       "      <td>4</td>\n",
       "      <td>365</td>\n",
       "      <td>31</td>\n",
       "      <td>52</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22748</th>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>XL</td>\n",
       "      <td>The Greek Pizza</td>\n",
       "      <td>25.50</td>\n",
       "      <td>1</td>\n",
       "      <td>the_greek_xl</td>\n",
       "      <td>Classic</td>\n",
       "      <td>4</td>\n",
       "      <td>365</td>\n",
       "      <td>31</td>\n",
       "      <td>52</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22372 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      order_date pizza_size                         pizza_name  unit_price  \\\n",
       "0     2015-01-01          L         The Barbecue Chicken Pizza       20.75   \n",
       "1     2015-01-01          L       The California Chicken Pizza       20.75   \n",
       "2     2015-01-01          L            The Chicken Pesto Pizza       20.75   \n",
       "3     2015-01-01          L           The Classic Deluxe Pizza       20.50   \n",
       "5     2015-01-01          L              The Four Cheese Pizza       17.95   \n",
       "...          ...        ...                                ...         ...   \n",
       "22744 2015-12-31          S            The Spicy Italian Pizza       12.50   \n",
       "22745 2015-12-31          S            The Spinach Pesto Pizza       12.50   \n",
       "22746 2015-12-31          S         The Spinach and Feta Pizza       12.00   \n",
       "22747 2015-12-31          S  The Vegetables + Vegetables Pizza       12.00   \n",
       "22748 2015-12-31         XL                    The Greek Pizza       25.50   \n",
       "\n",
       "       quantity  pizza_name_id pizza_category  day_of_week  day_of_year  \\\n",
       "0             6      bbq_ckn_l        Chicken            4            1   \n",
       "1             2     cali_ckn_l        Chicken            4            1   \n",
       "2             3    ckn_pesto_l        Chicken            4            1   \n",
       "3             1  classic_dlx_l        Classic            4            1   \n",
       "5             3  four_cheese_l         Veggie            4            1   \n",
       "...         ...            ...            ...          ...          ...   \n",
       "22744         3   spicy_ital_s        Supreme            4          365   \n",
       "22745         2   spin_pesto_s         Veggie            4          365   \n",
       "22746         3  spinach_fet_s         Veggie            4          365   \n",
       "22747         4   veggie_veg_s         Veggie            4          365   \n",
       "22748         1   the_greek_xl        Classic            4          365   \n",
       "\n",
       "       day_of_month week_of_year  month  \n",
       "0                 1           00      1  \n",
       "1                 1           00      1  \n",
       "2                 1           00      1  \n",
       "3                 1           00      1  \n",
       "5                 1           00      1  \n",
       "...             ...          ...    ...  \n",
       "22744            31           52     12  \n",
       "22745            31           52     12  \n",
       "22746            31           52     12  \n",
       "22747            31           52     12  \n",
       "22748            31           52     12  \n",
       "\n",
       "[22372 rows x 12 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extra_features_from_date(df_filtered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "1407c8b9-737c-4342-8e04-e86b1a54ba33",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filtered.drop(['order_date','pizza_name_id'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "c1564566-f3f8-403d-93d4-ace535674762",
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pizza_size</th>\n",
       "      <th>pizza_name</th>\n",
       "      <th>unit_price</th>\n",
       "      <th>quantity</th>\n",
       "      <th>pizza_category</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>week_of_year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L</td>\n",
       "      <td>The Barbecue Chicken Pizza</td>\n",
       "      <td>20.75</td>\n",
       "      <td>6</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  pizza_size                  pizza_name  unit_price  quantity pizza_category  \\\n",
       "0          L  The Barbecue Chicken Pizza       20.75         6        Chicken   \n",
       "\n",
       "   day_of_week  day_of_year  day_of_month week_of_year  month  \n",
       "0            4            1             1           00      1  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "f7f3201f-d451-4fa1-96cc-253aaa3f69e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pizza_size         object\n",
       "pizza_name         object\n",
       "unit_price        float64\n",
       "quantity            int64\n",
       "pizza_category     object\n",
       "day_of_week         int32\n",
       "day_of_year         int32\n",
       "day_of_month        int32\n",
       "week_of_year        int64\n",
       "month               int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "2bc39441-f34c-4080-8073-7ac56947928f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filtered['week_of_year'] = df_filtered['week_of_year'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "c9dc2e75-a64e-410f-9e97-f3cb3d62f29c",
   "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>pizza_size</th>\n",
       "      <th>pizza_name</th>\n",
       "      <th>unit_price</th>\n",
       "      <th>quantity</th>\n",
       "      <th>pizza_category</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>week_of_year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L</td>\n",
       "      <td>The Barbecue Chicken Pizza</td>\n",
       "      <td>20.75</td>\n",
       "      <td>6</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  pizza_size                  pizza_name  unit_price  quantity pizza_category  \\\n",
       "0          L  The Barbecue Chicken Pizza       20.75         6        Chicken   \n",
       "\n",
       "   day_of_week  day_of_year  day_of_month  week_of_year  month  \n",
       "0            4            1             1             0      1  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "76840b88-c67c-46e7-b578-3a1876c4b4f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# With Outliers: df_agg\n",
    "# Without Outliers: df_filtered"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eaddf9e-c71b-487a-b6d7-cdcefe19acfe",
   "metadata": {},
   "source": [
    "## Classic ML regression models "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "fb6343cf-0971-456d-99bb-061d2b9fe868",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "4d28d0ab-8611-4f9a-8214-cc69eda9452f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_encoded = pd.get_dummies(df_filtered, columns=['pizza_name', 'pizza_category'], drop_first=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "fdf2b1e9-22f0-4e78-b715-eab8bf2aeb3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label Encoding\n",
    "le = LabelEncoder()\n",
    "df_encoded['pizza_size'] = le.fit_transform(df_encoded['pizza_size'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "8d4ee792-8e5a-47ad-9b62-efaf48330440",
   "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>pizza_size</th>\n",
       "      <th>unit_price</th>\n",
       "      <th>quantity</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>week_of_year</th>\n",
       "      <th>month</th>\n",
       "      <th>pizza_name_The Barbecue Chicken Pizza</th>\n",
       "      <th>pizza_name_The Big Meat Pizza</th>\n",
       "      <th>...</th>\n",
       "      <th>pizza_name_The Spicy Italian Pizza</th>\n",
       "      <th>pizza_name_The Spinach Pesto Pizza</th>\n",
       "      <th>pizza_name_The Spinach Supreme Pizza</th>\n",
       "      <th>pizza_name_The Spinach and Feta Pizza</th>\n",
       "      <th>pizza_name_The Thai Chicken Pizza</th>\n",
       "      <th>pizza_name_The Vegetables + Vegetables Pizza</th>\n",
       "      <th>pizza_category_Chicken</th>\n",
       "      <th>pizza_category_Classic</th>\n",
       "      <th>pizza_category_Supreme</th>\n",
       "      <th>pizza_category_Veggie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>20.75</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>20.75</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>20.75</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>20.50</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>17.95</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   pizza_size  unit_price  quantity  day_of_week  day_of_year  day_of_month  \\\n",
       "0           0       20.75         6            4            1             1   \n",
       "1           0       20.75         2            4            1             1   \n",
       "2           0       20.75         3            4            1             1   \n",
       "3           0       20.50         1            4            1             1   \n",
       "5           0       17.95         3            4            1             1   \n",
       "\n",
       "   week_of_year  month  pizza_name_The Barbecue Chicken Pizza  \\\n",
       "0             0      1                                   True   \n",
       "1             0      1                                  False   \n",
       "2             0      1                                  False   \n",
       "3             0      1                                  False   \n",
       "5             0      1                                  False   \n",
       "\n",
       "   pizza_name_The Big Meat Pizza  ...  pizza_name_The Spicy Italian Pizza  \\\n",
       "0                          False  ...                               False   \n",
       "1                          False  ...                               False   \n",
       "2                          False  ...                               False   \n",
       "3                          False  ...                               False   \n",
       "5                          False  ...                               False   \n",
       "\n",
       "   pizza_name_The Spinach Pesto Pizza  pizza_name_The Spinach Supreme Pizza  \\\n",
       "0                               False                                 False   \n",
       "1                               False                                 False   \n",
       "2                               False                                 False   \n",
       "3                               False                                 False   \n",
       "5                               False                                 False   \n",
       "\n",
       "   pizza_name_The Spinach and Feta Pizza  pizza_name_The Thai Chicken Pizza  \\\n",
       "0                                  False                              False   \n",
       "1                                  False                              False   \n",
       "2                                  False                              False   \n",
       "3                                  False                              False   \n",
       "5                                  False                              False   \n",
       "\n",
       "   pizza_name_The Vegetables + Vegetables Pizza  pizza_category_Chicken  \\\n",
       "0                                         False                    True   \n",
       "1                                         False                    True   \n",
       "2                                         False                    True   \n",
       "3                                         False                   False   \n",
       "5                                         False                   False   \n",
       "\n",
       "   pizza_category_Classic  pizza_category_Supreme  pizza_category_Veggie  \n",
       "0                   False                   False                  False  \n",
       "1                   False                   False                  False  \n",
       "2                   False                   False                  False  \n",
       "3                    True                   False                  False  \n",
       "5                   False                   False                   True  \n",
       "\n",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "99f86b09-344b-4583-9ec0-e20ce99edfa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Define features and target\n",
    "X = df_encoded.drop('quantity', axis=1)  # Features\n",
    "y = df_encoded['quantity']  # Target\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "ad30b552-43d4-45ea-9900-8ea862717382",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression trained.\n",
      "Decision Tree trained.\n",
      "Random Forest trained.\n",
      "Gradient Boosting trained.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
    "\n",
    "# Initialize models\n",
    "models = {\n",
    "    'Linear Regression': LinearRegression(),\n",
    "    'Decision Tree': DecisionTreeRegressor(),\n",
    "    'Random Forest': RandomForestRegressor(),\n",
    "    'Gradient Boosting': GradientBoostingRegressor()\n",
    "}\n",
    "\n",
    "# Train models\n",
    "for name, model in models.items():\n",
    "    model.fit(X_train, y_train)\n",
    "    print(f'{name} trained.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "617290ad-1b23-41d7-82b1-6ca27821f038",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Linear Regression Metrics:\n",
      "MSE: 1.2809\n",
      "RMSE: 1.1318\n",
      "MAE: 0.8949\n",
      "R2: 0.1725\n",
      "\n",
      "Decision Tree Metrics:\n",
      "MSE: 2.4545\n",
      "RMSE: 1.5667\n",
      "MAE: 1.1236\n",
      "R2: -0.5858\n",
      "\n",
      "Random Forest Metrics:\n",
      "MSE: 1.3202\n",
      "RMSE: 1.1490\n",
      "MAE: 0.8988\n",
      "R2: 0.1470\n",
      "\n",
      "Gradient Boosting Metrics:\n",
      "MSE: 1.2145\n",
      "RMSE: 1.1021\n",
      "MAE: 0.8720\n",
      "R2: 0.2153\n"
     ]
    }
   ],
   "source": [
    "# Make predictions and evaluate each model\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "\n",
    "results = {}\n",
    "\n",
    "for name, model in models.items():\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    # Calculate metrics\n",
    "    mse = mean_squared_error(y_test, y_pred)\n",
    "    rmse = np.sqrt(mse)\n",
    "    mae = mean_absolute_error(y_test, y_pred)\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "    \n",
    "    # Store results\n",
    "    results[name] = {\n",
    "        'MSE': mse,\n",
    "        'RMSE': rmse,\n",
    "        'MAE': mae,\n",
    "        'R2': r2\n",
    "    }\n",
    "\n",
    "# Display results\n",
    "for name, metrics in results.items():\n",
    "    print(f'\\n{name} Metrics:')\n",
    "    for metric, value in metrics.items():\n",
    "        print(f'{metric}: {value:.4f}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "240615b8-8cdb-43c7-8306-46ca899ee3e9",
   "metadata": {},
   "source": [
    "### This is okay but in order to predict 91 pizza_types and for next 7 days would take a lot of time, thus we will look into another model that is more capable of dealing with time series problems"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "462b7b86-c994-4f08-be13-90cb7c6ef8ec",
   "metadata": {},
   "source": [
    "## LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "56b11c28-0c03-4d9b-a630-a6e206e6ff8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8e54e899-c158-4299-9a65-d081679cdc43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_date</th>\n",
       "      <th>pizza_name_id</th>\n",
       "      <th>quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>bbq_ckn_l</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>bbq_ckn_m</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>bbq_ckn_s</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>big_meat_s</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>calabrese_m</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  order_date pizza_name_id  quantity\n",
       "0 2015-01-01     bbq_ckn_l         6\n",
       "1 2015-01-01     bbq_ckn_m         4\n",
       "2 2015-01-01     bbq_ckn_s         1\n",
       "3 2015-01-01    big_meat_s         5\n",
       "4 2015-01-01   calabrese_m         1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cb1e75b1-4a69-4e19-aafc-1891dc1c16ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "      <th></th>\n",
       "      <th>bbq_ckn_l</th>\n",
       "      <th>bbq_ckn_m</th>\n",
       "      <th>bbq_ckn_s</th>\n",
       "      <th>big_meat_s</th>\n",
       "      <th>brie_carre_s</th>\n",
       "      <th>calabrese_l</th>\n",
       "      <th>calabrese_m</th>\n",
       "      <th>calabrese_s</th>\n",
       "      <th>cali_ckn_l</th>\n",
       "      <th>cali_ckn_m</th>\n",
       "      <th>...</th>\n",
       "      <th>thai_ckn_m</th>\n",
       "      <th>thai_ckn_s</th>\n",
       "      <th>the_greek_l</th>\n",
       "      <th>the_greek_m</th>\n",
       "      <th>the_greek_s</th>\n",
       "      <th>the_greek_xl</th>\n",
       "      <th>the_greek_xxl</th>\n",
       "      <th>veggie_veg_l</th>\n",
       "      <th>veggie_veg_m</th>\n",
       "      <th>veggie_veg_s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
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       "      <td>9.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-03</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 91 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            bbq_ckn_l  bbq_ckn_m  bbq_ckn_s  big_meat_s  brie_carre_s  \\\n",
       "order_date                                                              \n",
       "2015-01-01        6.0        4.0        1.0         5.0           NaN   \n",
       "2015-01-02        4.0        7.0        1.0         9.0           3.0   \n",
       "2015-01-03        2.0        NaN        NaN         3.0           1.0   \n",
       "2015-01-04        NaN        2.0        NaN         3.0           NaN   \n",
       "\n",
       "            calabrese_l  calabrese_m  calabrese_s  cali_ckn_l  cali_ckn_m  \\\n",
       "order_date                                                                  \n",
       "2015-01-01          NaN          1.0          NaN         2.0         1.0   \n",
       "2015-01-02          2.0          5.0          NaN         4.0         9.0   \n",
       "2015-01-03          NaN          1.0          NaN         1.0         4.0   \n",
       "2015-01-04          1.0          1.0          NaN         2.0         1.0   \n",
       "\n",
       "            ...  thai_ckn_m  thai_ckn_s  the_greek_l  the_greek_m  \\\n",
       "order_date  ...                                                     \n",
       "2015-01-01  ...         2.0         NaN          NaN          NaN   \n",
       "2015-01-02  ...         1.0         1.0          1.0          1.0   \n",
       "2015-01-03  ...         NaN         2.0          NaN          1.0   \n",
       "2015-01-04  ...         1.0         NaN          NaN          2.0   \n",
       "\n",
       "            the_greek_s  the_greek_xl  the_greek_xxl  veggie_veg_l  \\\n",
       "order_date                                                           \n",
       "2015-01-01          4.0           1.0            NaN           1.0   \n",
       "2015-01-02          NaN           2.0            NaN           1.0   \n",
       "2015-01-03          1.0           1.0            NaN           3.0   \n",
       "2015-01-04          2.0           2.0            NaN           NaN   \n",
       "\n",
       "            veggie_veg_m  veggie_veg_s  \n",
       "order_date                              \n",
       "2015-01-01           NaN           2.0  \n",
       "2015-01-02           NaN           NaN  \n",
       "2015-01-03           1.0           2.0  \n",
       "2015-01-04           2.0           3.0  \n",
       "\n",
       "[4 rows x 91 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_agg['order_date'] = pd.to_datetime(df_agg['order_date'], format='%Y-%m-%d')\n",
    "\n",
    "data = pd.pivot_table(\n",
    "           data    = df_agg,\n",
    "           values  = 'quantity',\n",
    "           index   = 'order_date',\n",
    "           columns = 'pizza_name_id'\n",
    "       )\n",
    "data.columns.name = None\n",
    "data.columns = [f\"{col}\" for col in data.columns]\n",
    "data = data.asfreq('1D')\n",
    "data = data.sort_index()\n",
    "data.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bdf3c72a-2922-4712-bc19-3e144c4f7b8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f700d0f6-7b6b-4e32-931a-bf0df3d1b020",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\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>bbq_ckn_l</th>\n",
       "      <th>bbq_ckn_m</th>\n",
       "      <th>bbq_ckn_s</th>\n",
       "      <th>big_meat_s</th>\n",
       "      <th>brie_carre_s</th>\n",
       "      <th>calabrese_l</th>\n",
       "      <th>calabrese_m</th>\n",
       "      <th>calabrese_s</th>\n",
       "      <th>cali_ckn_l</th>\n",
       "      <th>cali_ckn_m</th>\n",
       "      <th>...</th>\n",
       "      <th>thai_ckn_m</th>\n",
       "      <th>thai_ckn_s</th>\n",
       "      <th>the_greek_l</th>\n",
       "      <th>the_greek_m</th>\n",
       "      <th>the_greek_s</th>\n",
       "      <th>the_greek_xl</th>\n",
       "      <th>the_greek_xxl</th>\n",
       "      <th>veggie_veg_l</th>\n",
       "      <th>veggie_veg_m</th>\n",
       "      <th>veggie_veg_s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>2015-01-01</th>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-03</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-04</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 91 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            bbq_ckn_l  bbq_ckn_m  bbq_ckn_s  big_meat_s  brie_carre_s  \\\n",
       "order_date                                                              \n",
       "2015-01-01        6.0        4.0        1.0         5.0           0.0   \n",
       "2015-01-02        4.0        7.0        1.0         9.0           3.0   \n",
       "2015-01-03        2.0        0.0        0.0         3.0           1.0   \n",
       "2015-01-04        0.0        2.0        0.0         3.0           0.0   \n",
       "\n",
       "            calabrese_l  calabrese_m  calabrese_s  cali_ckn_l  cali_ckn_m  \\\n",
       "order_date                                                                  \n",
       "2015-01-01          0.0          1.0          0.0         2.0         1.0   \n",
       "2015-01-02          2.0          5.0          0.0         4.0         9.0   \n",
       "2015-01-03          0.0          1.0          0.0         1.0         4.0   \n",
       "2015-01-04          1.0          1.0          0.0         2.0         1.0   \n",
       "\n",
       "            ...  thai_ckn_m  thai_ckn_s  the_greek_l  the_greek_m  \\\n",
       "order_date  ...                                                     \n",
       "2015-01-01  ...         2.0         0.0          0.0          0.0   \n",
       "2015-01-02  ...         1.0         1.0          1.0          1.0   \n",
       "2015-01-03  ...         0.0         2.0          0.0          1.0   \n",
       "2015-01-04  ...         1.0         0.0          0.0          2.0   \n",
       "\n",
       "            the_greek_s  the_greek_xl  the_greek_xxl  veggie_veg_l  \\\n",
       "order_date                                                           \n",
       "2015-01-01          4.0           1.0            0.0           1.0   \n",
       "2015-01-02          0.0           2.0            0.0           1.0   \n",
       "2015-01-03          1.0           1.0            0.0           3.0   \n",
       "2015-01-04          2.0           2.0            0.0           0.0   \n",
       "\n",
       "            veggie_veg_m  veggie_veg_s  \n",
       "order_date                              \n",
       "2015-01-01           0.0           2.0  \n",
       "2015-01-02           0.0           0.0  \n",
       "2015-01-03           1.0           2.0  \n",
       "2015-01-04           2.0           3.0  \n",
       "\n",
       "[4 rows x 91 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "523168fe-cbd9-4807-b21d-a343ab7082b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import skforecast\n",
    "from skforecast.plot import set_dark_theme\n",
    "\n",
    "# Plot time series for first 4 pizzas alone \n",
    "# ======================================================================================\n",
    "set_dark_theme()\n",
    "fig, axs = plt.subplots(4, 1, figsize=(7, 7), sharex=True)\n",
    "data.iloc[:, :4].plot(\n",
    "    legend   = True,\n",
    "    subplots = True, \n",
    "    title    = 'Pizza Quantities',\n",
    "    ax       = axs, \n",
    ")\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5390d4ab-3523-42c9-a4a1-d370cd09ed4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/pizzaproj/lib/python3.11/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m191/285\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 793us/step - loss: 0.0642"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/pizzaproj/lib/python3.11/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:122: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 787us/step - loss: 0.0592\n",
      "Epoch 2/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 789us/step - loss: 0.0406\n",
      "Epoch 3/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 787us/step - loss: 0.0403\n",
      "Epoch 4/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 777us/step - loss: 0.0397\n",
      "Epoch 5/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 775us/step - loss: 0.0395\n",
      "Epoch 6/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 781us/step - loss: 0.0387\n",
      "Epoch 7/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 855us/step - loss: 0.0382\n",
      "Epoch 8/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 839us/step - loss: 0.0378\n",
      "Epoch 9/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 822us/step - loss: 0.0375\n",
      "Epoch 10/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 807us/step - loss: 0.0374\n",
      "Epoch 11/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 798us/step - loss: 0.0373\n",
      "Epoch 12/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 791us/step - loss: 0.0363\n",
      "Epoch 13/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 785us/step - loss: 0.0363\n",
      "Epoch 14/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 791us/step - loss: 0.0367\n",
      "Epoch 15/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 955us/step - loss: 0.0352\n",
      "Epoch 16/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 793us/step - loss: 0.0352\n",
      "Epoch 17/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 780us/step - loss: 0.0352\n",
      "Epoch 18/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 782us/step - loss: 0.0343\n",
      "Epoch 19/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 788us/step - loss: 0.0335\n",
      "Epoch 20/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 781us/step - loss: 0.0342\n"
     ]
    }
   ],
   "source": [
    "# Normalize the data\n",
    "scaler = MinMaxScaler()\n",
    "scaled_data = scaler.fit_transform(data)\n",
    "\n",
    "# Define sequence length\n",
    "sequence_length = 7\n",
    "\n",
    "# Split data into train and test sets\n",
    "def train_test_split_sequences(data, sequence_length, test_size=0.2):\n",
    "    n = len(data)\n",
    "    split_idx = int(n * (1 - test_size))\n",
    "    \n",
    "    train_data = data[:split_idx]\n",
    "    test_data = data[split_idx:]\n",
    "    \n",
    "    return train_data, test_data\n",
    "\n",
    "# Function to create sequences\n",
    "def create_sequences(data, sequence_length):\n",
    "    generator = TimeseriesGenerator(data, data, length=sequence_length, batch_size=1)\n",
    "    return generator\n",
    "\n",
    "# Function to define and train LSTM model\n",
    "def train_lstm_model(train_data, sequence_length):\n",
    "    generator = create_sequences(train_data, sequence_length)\n",
    "    \n",
    "    model = Sequential([\n",
    "        LSTM(32, activation='relu', input_shape=(sequence_length, train_data.shape[1])),\n",
    "        Dense(train_data.shape[1])\n",
    "    ])\n",
    "    \n",
    "    model.compile(optimizer='adam', loss='mse')\n",
    "    model.fit(generator, epochs=20, verbose=1)\n",
    "    \n",
    "    return model\n",
    "\n",
    "\n",
    "def predict_next_7_days(model, data, sequence_length, scaler):\n",
    "    predictions = []\n",
    "    recent_data = data[-sequence_length:]\n",
    "    \n",
    "    for _ in range(7):\n",
    "        input_sequence = np.expand_dims(recent_data, axis=0)  # Shape: (1, sequence_length, number_of_features)\n",
    "        pred_scaled = model.predict(input_sequence)\n",
    "        pred = scaler.inverse_transform(pred_scaled.reshape(-1, pred_scaled.shape[-1]))  # Reshape for inverse_transform\n",
    "        predictions.append(pred[-1])  # Append the last prediction\n",
    "        \n",
    "        # Update the recent data with the new prediction\n",
    "        recent_data = np.append(recent_data[1:], np.expand_dims(pred_scaled[0], axis=0), axis=0)\n",
    "    \n",
    "    return np.array(predictions)\n",
    "    \n",
    "# Train-test split\n",
    "train_data, test_data = train_test_split_sequences(scaled_data, sequence_length)\n",
    "\n",
    "# Train the model\n",
    "model = train_lstm_model(train_data, sequence_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "847075d9-520f-4278-9b2a-22f24e0137d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to create sequences for test data\n",
    "def create_sequences_for_test(data, sequence_length):\n",
    "    generator = TimeseriesGenerator(data, data, length=sequence_length, batch_size=1, shuffle=False)\n",
    "    return generator\n",
    "\n",
    "# Create sequences for test data\n",
    "test_generator = create_sequences_for_test(test_data, sequence_length)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "245a35d9-e6f6-40ff-bd85-01a877f18e38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "Mean Squared Error (MSE): 0.04366028539377705\n",
      "Mean Absolute Error (MAE): 0.16290040664158195\n"
     ]
    }
   ],
   "source": [
    "# Get true values and predictions\n",
    "def get_predictions_and_true_values(model, generator):\n",
    "    true_values = []\n",
    "    predictions = []\n",
    "    \n",
    "    for i in range(len(generator)):\n",
    "        X, y = generator[i]\n",
    "        y_pred = model.predict(X)\n",
    "        \n",
    "        true_values.append(y.flatten())\n",
    "        predictions.append(y_pred.flatten())\n",
    "    \n",
    "    return np.array(true_values), np.array(predictions)\n",
    "\n",
    "true_values, predictions = get_predictions_and_true_values(model, test_generator)\n",
    "\n",
    "# Calculate MSE and MAE\n",
    "mse = mean_squared_error(true_values, predictions)\n",
    "mae = mean_absolute_error(true_values, predictions)\n",
    "\n",
    "print(f'Mean Squared Error (MSE): {mse}')\n",
    "print(f'Mean Absolute Error (MAE): {mae}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "fedb1ce0-ea65-4c0a-97d4-17400173e53c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Scale Mean Squared Error (MSE): 1.9312354312354314\n",
      "Original Scale Mean Absolute Error (MAE): 0.9771894771894774\n"
     ]
    }
   ],
   "source": [
    "# Inverse transform to original scale if needed\n",
    "true_values_original = scaler.inverse_transform(true_values)\n",
    "predictions_original = scaler.inverse_transform(predictions)\n",
    "\n",
    "round_predictions = np.round(predictions_original).astype(int)\n",
    "\n",
    "# Recalculate MSE and MAE in the original scale\n",
    "mse_original = mean_squared_error(true_values_original, round_predictions)\n",
    "mae_original = mean_absolute_error(true_values_original, round_predictions)\n",
    "\n",
    "print(f'Original Scale Mean Squared Error (MSE): {mse_original}')\n",
    "print(f'Original Scale Mean Absolute Error (MAE): {mae_original}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "bd2f3ae9-c4ed-4f63-85d9-e9993f8dfec1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 1, 1, ..., 1, 1, 1],\n",
       "       [3, 2, 2, ..., 2, 1, 1],\n",
       "       [3, 1, 1, ..., 2, 1, 2],\n",
       "       ...,\n",
       "       [3, 2, 1, ..., 1, 1, 1],\n",
       "       [3, 2, 2, ..., 1, 1, 1],\n",
       "       [3, 2, 2, ..., 1, 2, 1]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "46b2a5ec-03f1-40f8-96f7-452632fac842",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/pizzaproj/lib/python3.11/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m 79/285\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0760"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/pizzaproj/lib/python3.11/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:122: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0581\n",
      "Epoch 2/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0405\n",
      "Epoch 3/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 898us/step - loss: 0.0399\n",
      "Epoch 4/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0407\n",
      "Epoch 5/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 855us/step - loss: 0.0396\n",
      "Epoch 6/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 931us/step - loss: 0.0390\n",
      "Epoch 7/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 843us/step - loss: 0.0381\n",
      "Epoch 8/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 857us/step - loss: 0.0383\n",
      "Epoch 9/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 937us/step - loss: 0.0381\n",
      "Epoch 10/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 854us/step - loss: 0.0382\n",
      "Epoch 11/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 859us/step - loss: 0.0368\n",
      "Epoch 12/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 914us/step - loss: 0.0363\n",
      "Epoch 13/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 857us/step - loss: 0.0358\n",
      "Epoch 14/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 968us/step - loss: 0.0359\n",
      "Epoch 15/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 983us/step - loss: 0.0352\n",
      "Epoch 16/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 855us/step - loss: 0.0350\n",
      "Epoch 17/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 855us/step - loss: 0.0347\n",
      "Epoch 18/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0340\n",
      "Epoch 19/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0339 \n",
      "Epoch 20/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 838us/step - loss: 0.0335\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "Sequence length : 7\n",
      "LSTM Unit size  : 32\n",
      "Mean Squared Error (MSE): 0.044959221039186786\n",
      "Mean Absolute Error (MAE): 0.16617868278986317\n",
      "Original Scale Mean Squared Error (MSE): 1.9911754911754913\n",
      "Original Scale Mean Absolute Error (MAE): 0.9991674991674993\n",
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/pizzaproj/lib/python3.11/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m113/285\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0696  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/pizzaproj/lib/python3.11/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:122: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0575\n",
      "Epoch 2/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0398\n",
      "Epoch 3/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0399\n",
      "Epoch 4/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0391\n",
      "Epoch 5/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0388\n",
      "Epoch 6/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0373\n",
      "Epoch 7/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0372\n",
      "Epoch 8/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0376\n",
      "Epoch 9/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0359\n",
      "Epoch 10/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0353\n",
      "Epoch 11/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0347\n",
      "Epoch 12/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0332\n",
      "Epoch 13/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0330\n",
      "Epoch 14/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0318\n",
      "Epoch 15/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0320\n",
      "Epoch 16/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0309\n",
      "Epoch 17/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0301\n",
      "Epoch 18/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0294\n",
      "Epoch 19/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0281\n",
      "Epoch 20/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0279\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "Sequence length : 7\n",
      "LSTM Unit size  : 64\n",
      "Mean Squared Error (MSE): 0.04679548173869264\n",
      "Mean Absolute Error (MAE): 0.1684558199036879\n",
      "Original Scale Mean Squared Error (MSE): 2.0566100566100562\n",
      "Original Scale Mean Absolute Error (MAE): 1.010989010989011\n",
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/pizzaproj/lib/python3.11/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m 86/285\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0542  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/pizzaproj/lib/python3.11/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:122: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0476\n",
      "Epoch 2/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0411\n",
      "Epoch 3/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0401\n",
      "Epoch 4/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0402\n",
      "Epoch 5/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0379\n",
      "Epoch 6/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0385\n",
      "Epoch 7/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0369\n",
      "Epoch 8/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0355\n",
      "Epoch 9/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0352\n",
      "Epoch 10/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0320\n",
      "Epoch 11/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0319\n",
      "Epoch 12/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0310\n",
      "Epoch 13/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0295\n",
      "Epoch 14/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0278\n",
      "Epoch 15/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0273\n",
      "Epoch 16/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0262\n",
      "Epoch 17/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0250\n",
      "Epoch 18/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0239\n",
      "Epoch 19/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0227\n",
      "Epoch 20/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0220\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "Sequence length : 7\n",
      "LSTM Unit size  : 128\n",
      "Mean Squared Error (MSE): 0.04597762693142303\n",
      "Mean Absolute Error (MAE): 0.166531814971772\n",
      "Original Scale Mean Squared Error (MSE): 2.010989010989011\n",
      "Original Scale Mean Absolute Error (MAE): 1.000999000999001\n",
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/pizzaproj/lib/python3.11/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m 24/285\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 0.0756"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/pizzaproj/lib/python3.11/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:122: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - loss: 0.0488\n",
      "Epoch 2/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0413\n",
      "Epoch 3/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0394\n",
      "Epoch 4/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0394\n",
      "Epoch 5/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0389\n",
      "Epoch 6/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0382\n",
      "Epoch 7/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - loss: 0.0363\n",
      "Epoch 8/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - loss: 0.0356\n",
      "Epoch 9/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0339\n",
      "Epoch 10/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - loss: 0.0315\n",
      "Epoch 11/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0296\n",
      "Epoch 12/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - loss: 0.0279\n",
      "Epoch 13/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - loss: 0.0259\n",
      "Epoch 14/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0241\n",
      "Epoch 15/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0223\n",
      "Epoch 16/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0207\n",
      "Epoch 17/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0196\n",
      "Epoch 18/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0185\n",
      "Epoch 19/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0172\n",
      "Epoch 20/20\n",
      "\u001b[1m285/285\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - loss: 0.0165\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "Sequence length : 7\n",
      "LSTM Unit size  : 256\n",
      "Mean Squared Error (MSE): 0.04892619599508172\n",
      "Mean Absolute Error (MAE): 0.17021360062827928\n",
      "Original Scale Mean Squared Error (MSE): 2.135697635697636\n",
      "Original Scale Mean Absolute Error (MAE): 1.0241425241425242\n",
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/pizzaproj/lib/python3.11/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m101/278\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0783  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/pizzaproj/lib/python3.11/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:122: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0619\n",
      "Epoch 2/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0412\n",
      "Epoch 3/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0407\n",
      "Epoch 4/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0395\n",
      "Epoch 5/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0389\n",
      "Epoch 6/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0389\n",
      "Epoch 7/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0377\n",
      "Epoch 8/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0387\n",
      "Epoch 9/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0379\n",
      "Epoch 10/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0379\n",
      "Epoch 11/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0381\n",
      "Epoch 12/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0365\n",
      "Epoch 13/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0362\n",
      "Epoch 14/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0353\n",
      "Epoch 15/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0352\n",
      "Epoch 16/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0352\n",
      "Epoch 17/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0344\n",
      "Epoch 18/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0335\n",
      "Epoch 19/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0337\n",
      "Epoch 20/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0343\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "Sequence length : 14\n",
      "LSTM Unit size  : 32\n",
      "Mean Squared Error (MSE): 0.04363861077103037\n",
      "Mean Absolute Error (MAE): 0.16265608921313685\n",
      "Original Scale Mean Squared Error (MSE): 1.9240081951946355\n",
      "Original Scale Mean Absolute Error (MAE): 0.9763456882100949\n",
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/pizzaproj/lib/python3.11/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m 81/278\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0657  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/pizzaproj/lib/python3.11/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:122: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0532\n",
      "Epoch 2/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0409\n",
      "Epoch 3/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0407\n",
      "Epoch 4/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0393\n",
      "Epoch 5/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0412\n",
      "Epoch 6/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0383\n",
      "Epoch 7/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0382\n",
      "Epoch 8/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0379\n",
      "Epoch 9/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0374\n",
      "Epoch 10/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0360\n",
      "Epoch 11/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0362\n",
      "Epoch 12/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0350\n",
      "Epoch 13/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - loss: 0.0341\n",
      "Epoch 14/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - loss: 0.0334\n",
      "Epoch 15/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0328\n",
      "Epoch 16/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0326\n",
      "Epoch 17/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0313\n",
      "Epoch 18/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0307\n",
      "Epoch 19/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0302\n",
      "Epoch 20/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0301\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "Sequence length : 14\n",
      "LSTM Unit size  : 64\n",
      "Mean Squared Error (MSE): 0.04655334699265301\n",
      "Mean Absolute Error (MAE): 0.1680637266016998\n",
      "Original Scale Mean Squared Error (MSE): 2.0387409200968523\n",
      "Original Scale Mean Absolute Error (MAE): 1.0158316260011175\n",
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/pizzaproj/lib/python3.11/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m 45/278\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0646  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/pizzaproj/lib/python3.11/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:122: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - loss: 0.0492\n",
      "Epoch 2/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0409\n",
      "Epoch 3/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0415\n",
      "Epoch 4/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0383\n",
      "Epoch 5/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0386\n",
      "Epoch 6/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0391\n",
      "Epoch 7/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0366\n",
      "Epoch 8/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - loss: 0.0364\n",
      "Epoch 9/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 0.0356\n",
      "Epoch 10/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0339\n",
      "Epoch 11/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0327\n",
      "Epoch 12/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0307\n",
      "Epoch 13/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0305\n",
      "Epoch 14/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0299\n",
      "Epoch 15/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0281\n",
      "Epoch 16/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0271\n",
      "Epoch 17/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0261\n",
      "Epoch 18/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0256\n",
      "Epoch 19/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0248\n",
      "Epoch 20/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0235\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "Sequence length : 14\n",
      "LSTM Unit size  : 128\n",
      "Mean Squared Error (MSE): 0.049179749790479746\n",
      "Mean Absolute Error (MAE): 0.1735110013904538\n",
      "Original Scale Mean Squared Error (MSE): 2.134289439374185\n",
      "Original Scale Mean Absolute Error (MAE): 1.0502886943564909\n",
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/pizzaproj/lib/python3.11/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m 28/278\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0652"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/pizzaproj/lib/python3.11/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:122: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0481\n",
      "Epoch 2/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0413\n",
      "Epoch 3/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 0.0399\n",
      "Epoch 4/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0401\n",
      "Epoch 5/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0385\n",
      "Epoch 6/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 0.0381\n",
      "Epoch 7/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0367\n",
      "Epoch 8/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0362\n",
      "Epoch 9/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0332\n",
      "Epoch 10/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 0.0326\n",
      "Epoch 11/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 0.0309\n",
      "Epoch 12/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 0.0298\n",
      "Epoch 13/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0271\n",
      "Epoch 14/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0260\n",
      "Epoch 15/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0241\n",
      "Epoch 16/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0236\n",
      "Epoch 17/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0217\n",
      "Epoch 18/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0209\n",
      "Epoch 19/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0195\n",
      "Epoch 20/20\n",
      "\u001b[1m278/278\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0190\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "Sequence length : 14\n",
      "LSTM Unit size  : 256\n",
      "Mean Squared Error (MSE): 0.04991849869491892\n",
      "Mean Absolute Error (MAE): 0.17202820963055745\n",
      "Original Scale Mean Squared Error (MSE): 2.2123300428385178\n",
      "Original Scale Mean Absolute Error (MAE): 1.0441422983795865\n",
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/pizzaproj/lib/python3.11/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m 68/262\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0795  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/pizzaproj/lib/python3.11/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:122: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0599\n",
      "Epoch 2/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0408\n",
      "Epoch 3/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0394\n",
      "Epoch 4/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0398\n",
      "Epoch 5/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0393\n",
      "Epoch 6/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0383\n",
      "Epoch 7/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0381\n",
      "Epoch 8/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0384\n",
      "Epoch 9/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0384\n",
      "Epoch 10/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0377\n",
      "Epoch 11/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0371\n",
      "Epoch 12/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0355\n",
      "Epoch 13/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0366\n",
      "Epoch 14/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0362\n",
      "Epoch 15/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0352\n",
      "Epoch 16/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0359\n",
      "Epoch 17/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0338\n",
      "Epoch 18/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0345\n",
      "Epoch 19/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0338\n",
      "Epoch 20/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0332\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "Sequence length : 30\n",
      "LSTM Unit size  : 32\n",
      "Mean Squared Error (MSE): 0.0445336193864653\n",
      "Mean Absolute Error (MAE): 0.16506913432948792\n",
      "Original Scale Mean Squared Error (MSE): 1.9938665985177615\n",
      "Original Scale Mean Absolute Error (MAE): 0.9982110912343468\n",
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/pizzaproj/lib/python3.11/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m 51/262\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.0840  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/pizzaproj/lib/python3.11/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:122: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0570\n",
      "Epoch 2/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0423\n",
      "Epoch 3/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0403\n",
      "Epoch 4/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0404\n",
      "Epoch 5/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0388\n",
      "Epoch 6/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0381\n",
      "Epoch 7/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0381\n",
      "Epoch 8/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0379\n",
      "Epoch 9/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0369\n",
      "Epoch 10/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0365\n",
      "Epoch 11/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0362\n",
      "Epoch 12/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0345\n",
      "Epoch 13/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0343\n",
      "Epoch 14/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0337\n",
      "Epoch 15/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0326\n",
      "Epoch 16/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0316\n",
      "Epoch 17/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0308\n",
      "Epoch 18/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0306\n",
      "Epoch 19/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0297\n",
      "Epoch 20/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0291\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "Sequence length : 30\n",
      "LSTM Unit size  : 64\n",
      "Mean Squared Error (MSE): 0.04903005467569228\n",
      "Mean Absolute Error (MAE): 0.1724208568472527\n",
      "Original Scale Mean Squared Error (MSE): 2.1487349859442886\n",
      "Original Scale Mean Absolute Error (MAE): 1.0391004344492718\n",
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/pizzaproj/lib/python3.11/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m 28/262\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0725"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/pizzaproj/lib/python3.11/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:122: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0498\n",
      "Epoch 2/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0406\n",
      "Epoch 3/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0399\n",
      "Epoch 4/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0392\n",
      "Epoch 5/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0382\n",
      "Epoch 6/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0381\n",
      "Epoch 7/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0376\n",
      "Epoch 8/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0360\n",
      "Epoch 9/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0345\n",
      "Epoch 10/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0330\n",
      "Epoch 11/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0322\n",
      "Epoch 12/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0305\n",
      "Epoch 13/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0303\n",
      "Epoch 14/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0276\n",
      "Epoch 15/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0272\n",
      "Epoch 16/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - loss: 0.0257\n",
      "Epoch 17/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0241\n",
      "Epoch 18/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0238\n",
      "Epoch 19/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0231\n",
      "Epoch 20/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0224\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "Sequence length : 30\n",
      "LSTM Unit size  : 128\n",
      "Mean Squared Error (MSE): 0.05345342819678626\n",
      "Mean Absolute Error (MAE): 0.178974757269334\n",
      "Original Scale Mean Squared Error (MSE): 2.3746486072067468\n",
      "Original Scale Mean Absolute Error (MAE): 1.078967544083823\n",
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/pizzaproj/lib/python3.11/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m 16/262\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - loss: 0.0821"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/pizzaproj/lib/python3.11/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:122: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 13ms/step - loss: 0.0486\n",
      "Epoch 2/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 13ms/step - loss: 0.0407\n",
      "Epoch 3/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 13ms/step - loss: 0.0394\n",
      "Epoch 4/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 13ms/step - loss: 0.0393\n",
      "Epoch 5/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 13ms/step - loss: 0.0382\n",
      "Epoch 6/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - loss: 0.0390\n",
      "Epoch 7/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 13ms/step - loss: 0.0357\n",
      "Epoch 8/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - loss: 0.0351\n",
      "Epoch 9/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - loss: 0.0346\n",
      "Epoch 10/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - loss: 0.0315\n",
      "Epoch 11/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - loss: 0.0292\n",
      "Epoch 12/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 13ms/step - loss: 0.0275\n",
      "Epoch 13/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - loss: 0.0257\n",
      "Epoch 14/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - loss: 0.0238\n",
      "Epoch 15/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 13ms/step - loss: 0.0223\n",
      "Epoch 16/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 13ms/step - loss: 0.0205\n",
      "Epoch 17/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - loss: 0.0195\n",
      "Epoch 18/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 13ms/step - loss: 0.0186\n",
      "Epoch 19/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 13ms/step - loss: 0.0168\n",
      "Epoch 20/20\n",
      "\u001b[1m262/262\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 13ms/step - loss: 0.0163\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "Sequence length : 30\n",
      "LSTM Unit size  : 256\n",
      "Mean Squared Error (MSE): 0.05239980478842403\n",
      "Mean Absolute Error (MAE): 0.17616115060487486\n",
      "Original Scale Mean Squared Error (MSE): 2.3084589828775877\n",
      "Original Scale Mean Absolute Error (MAE): 1.0618451316125734\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, Dense\n",
    "\n",
    "# Hyper parameter tuning \n",
    "\n",
    "# Define sequence length\n",
    "# Function to create sequences\n",
    "def create_sequences(data, sequence_length):\n",
    "    generator = TimeseriesGenerator(data, data, length=sequence_length, batch_size=1)\n",
    "    return generator\n",
    "\n",
    "# Function to create sequences for test data\n",
    "def create_sequences_for_test(data, sequence_length):\n",
    "    generator = TimeseriesGenerator(data, data, length=sequence_length, batch_size=1, shuffle=False)\n",
    "    return generator\n",
    "\n",
    "\n",
    "# Function to define and train LSTM model\n",
    "def train_lstm_model1(train_data, sequence_length,unit):\n",
    "    generator = create_sequences(train_data, sequence_length)\n",
    "    \n",
    "    model = Sequential([\n",
    "        LSTM(unit, activation='relu', input_shape=(sequence_length, train_data.shape[1])),\n",
    "        Dense(train_data.shape[1])\n",
    "    ])\n",
    "    \n",
    "    model.compile(optimizer='adam', loss='mse')\n",
    "    model.fit(generator, epochs=20, verbose=1)\n",
    "    \n",
    "    return model\n",
    "\n",
    "# Normalize the data\n",
    "scaler = MinMaxScaler()\n",
    "scaled_data = scaler.fit_transform(data)\n",
    "\n",
    "# Split data into train and test sets\n",
    "def train_test_split_sequences(data, sequence_length, test_size=0.2):\n",
    "    n = len(data)\n",
    "    split_idx = int(n * (1 - test_size))\n",
    "    \n",
    "    train_data = data[:split_idx]\n",
    "    test_data = data[split_idx:]\n",
    "    \n",
    "    return train_data, test_data\n",
    "\n",
    "# Get true values and predictions\n",
    "def get_predictions_and_true_values(model, generator):\n",
    "    true_values = []\n",
    "    predictions = []\n",
    "    \n",
    "    for i in range(len(generator)):\n",
    "        X, y = generator[i]\n",
    "        y_pred = model.predict(X)\n",
    "        \n",
    "        true_values.append(y.flatten())\n",
    "        predictions.append(y_pred.flatten())\n",
    "    \n",
    "    return np.array(true_values), np.array(predictions)\n",
    "\n",
    "sequence_length = [7,14,30]\n",
    "units = [32,64,128,256 ]\n",
    "\n",
    "for s in sequence_length:\n",
    "    for i in units:\n",
    "\n",
    "    \n",
    "        train_data, test_data = train_test_split_sequences(scaled_data, s)\n",
    "        \n",
    "        # Train the modeloaded_mo\n",
    "        model = train_lstm_model1(train_data, s,i)\n",
    "        \n",
    "        \n",
    "        # Create sequences for test data\n",
    "        test_generator = create_sequences_for_test(test_data, s)\n",
    "        \n",
    "        \n",
    "        true_values, predictions = get_predictions_and_true_values(model, test_generator)\n",
    "        \n",
    "        # Calculate MSE and MAE\n",
    "        mse = mean_squared_error(true_values, predictions)\n",
    "        mae = mean_absolute_error(true_values, predictions)\n",
    "\n",
    "        print(f'Sequence length : {s}')\n",
    "        print(f'LSTM Unit size  : {i}')\n",
    "        print(f'Mean Squared Error (MSE): {mse}')\n",
    "        print(f'Mean Absolute Error (MAE): {mae}')\n",
    "        \n",
    "        \n",
    "        # Inverse transform to original scale if needed\n",
    "        true_values_original = scaler.inverse_transform(true_values)\n",
    "        predictions_original = scaler.inverse_transform(predictions)\n",
    "        \n",
    "        round_predictions = np.round(predictions_original).astype(int)\n",
    "        \n",
    "        # Recalculate MSE and MAE in the original scale\n",
    "        mse_original = mean_squared_error(true_values_original, round_predictions)\n",
    "        mae_original = mean_absolute_error(true_values_original, round_predictions)\n",
    "        \n",
    "        print(f'Original Scale Mean Squared Error (MSE): {mse_original}')\n",
    "        print(f'Original Scale Mean Absolute Error (MAE): {mae_original}')\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e4e2efdf-81a9-460d-a570-1438f4b25f38",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save model \n",
    "model.save(\"lstm_pizza_model.keras\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d503e26b-e19d-4b32-9a89-945190900cc9",
   "metadata": {},
   "source": [
    "## Pizza quantities predictions for next 7 days and it's ingridients quantity in grams as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c12c0603-ebd0-4900-aab0-9ab96fd39db9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/pizzaproj/lib/python3.11/site-packages/keras/src/saving/saving_lib.py:713: UserWarning: Skipping variable loading for optimizer 'rmsprop', because it has 7 variables whereas the saved optimizer has 12 variables. \n",
      "  saveable.load_own_variables(weights_store.get(inner_path))\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "data = pd.read_excel('data_time_series.xlsx', index_col=0)\n",
    "data.head()\n",
    "\n",
    "# Normalize the data\n",
    "scaler = MinMaxScaler()\n",
    "scaled_data = scaler.fit_transform(data)\n",
    "\n",
    "# Define sequence length\n",
    "sequence_length = 7\n",
    "\n",
    "loaded_model = load_model(\"lstm_pizza_model.keras\")\n",
    "\n",
    "def predict_next_7_days(model, data, sequence_length, scaler):\n",
    "    predictions = []\n",
    "    recent_data = data[-sequence_length:]\n",
    "    \n",
    "    for _ in range(7):\n",
    "        input_sequence = np.expand_dims(recent_data, axis=0)  # Shape: (1, sequence_length, number_of_features)\n",
    "        pred_scaled = model.predict(input_sequence)\n",
    "        pred = scaler.inverse_transform(pred_scaled.reshape(-1, pred_scaled.shape[-1]))  # Reshape for inverse_transform\n",
    "        predictions.append(pred[-1])  # Append the last prediction\n",
    "        \n",
    "        # Update the recent data with the new prediction\n",
    "        recent_data = np.append(recent_data[1:], np.expand_dims(pred_scaled[0], axis=0), axis=0)\n",
    "    \n",
    "    return np.array(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "d12c6d03-77db-4c7d-9166-f90804cbae07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "Predictions for the next 7 days: [[ 2.6355176e+00  1.7537613e+00  1.7666330e+00  5.5341644e+00\n",
      "   1.1814649e+00  1.5174201e+00  8.6733031e-01  3.9141786e-01\n",
      "   2.3274240e+00  2.9183025e+00  1.2585834e+00  1.4803524e-01\n",
      "   2.1820722e+00  3.5896385e-01  7.5418067e-01  8.0369866e-01\n",
      "   1.0575402e+00  1.2587126e+00  4.0918531e+00  2.3540034e+00\n",
      "   3.6992073e+00  3.3481240e+00  1.7917469e+00  5.5395818e-01\n",
      "   7.2756582e-01  1.6058552e+00  4.1292663e+00  1.4609780e+00\n",
      "   2.6274123e+00  1.9955482e+00  1.3640611e+00  7.1346569e-01\n",
      "   2.2936611e+00  3.5791187e+00  4.6545792e-01  4.5616278e-01\n",
      "   1.1533172e+00  9.4946921e-01  9.8053330e-01  6.2107670e-01\n",
      "   1.2635487e-01  2.5766242e+00  2.1175599e+00  5.0392711e-01\n",
      "   9.6629977e-01  1.5072715e+00  1.8367465e+00  1.3947397e+00\n",
      "   1.6122599e+00  2.1622605e+00  2.2860417e+00  1.8731023e+00\n",
      "   2.1117995e+00  1.5060275e+00  8.7279469e-01  8.0665094e-01\n",
      "   7.8836238e-01  1.6431596e+00  1.4462750e+00  2.0235617e+00\n",
      "   1.4060105e+00  1.2761648e+00  1.1271380e+00  9.2778397e-01\n",
      "   1.0162820e+00  3.3174443e+00  1.4411972e+00  1.3317077e+00\n",
      "   3.9212358e+00  1.4324008e+00  1.6446127e+00  8.7478030e-01\n",
      "   9.6908128e-01  1.3390172e+00  9.8187321e-01  1.1989012e+00\n",
      "   1.8682861e+00  5.0502825e-01  8.0307329e-01  1.3554595e+00\n",
      "   5.4723663e+00  1.1619674e+00  1.8256404e+00  5.4555374e-01\n",
      "   9.5359850e-01  7.5111300e-01  1.9696654e+00 -3.8829181e-02\n",
      "   1.2892503e+00  2.1627076e+00  1.4818006e+00]\n",
      " [ 2.1193638e+00  1.7789809e+00  1.5037854e+00  4.9518781e+00\n",
      "   1.2652999e+00  7.9183996e-01  1.1844246e+00  1.5256312e-01\n",
      "   2.3568673e+00  2.2706294e+00  1.1401207e+00  4.2655957e-01\n",
      "   1.9309356e+00  2.6567101e-01  8.4004939e-01  9.2754447e-01\n",
      "   9.3703568e-01  7.2136289e-01  3.3503230e+00  2.1188614e+00\n",
      "   3.2561197e+00  3.4991505e+00  1.5924826e+00  1.8052681e-01\n",
      "   6.1332583e-01  1.5673599e+00  2.8468993e+00  1.2943587e+00\n",
      "   2.5367088e+00  1.5700274e+00  8.8526738e-01  6.4232731e-01\n",
      "   1.8802458e+00  2.5761590e+00  5.8963537e-01  5.6774288e-01\n",
      "   1.0880697e+00  8.6367327e-01  1.1409352e+00  6.0806012e-01\n",
      "   3.8776273e-01  1.6448327e+00  1.3020819e+00  5.2464616e-01\n",
      "   1.2718232e+00  1.4212579e+00  1.1475409e+00  9.1244662e-01\n",
      "   1.1852434e+00  1.4492564e+00  1.9956496e+00  2.1718776e+00\n",
      "   1.8124764e+00  1.7169315e+00  1.0423353e+00  6.4835548e-01\n",
      "   9.4778454e-01  1.4026835e+00  1.3214545e+00  1.1568410e+00\n",
      "   9.1762447e-01  1.6684722e+00  1.0452906e+00  8.4819221e-01\n",
      "   7.2833037e-01  2.6456347e+00  1.3049062e+00  9.2711502e-01\n",
      "   2.8081439e+00  1.0302057e+00  1.1422925e+00  1.0443722e+00\n",
      "   8.1631351e-01  8.0386734e-01  1.1083755e+00  1.4523733e+00\n",
      "   1.2607163e+00  8.1319147e-01  7.5413537e-01  1.1928759e+00\n",
      "   3.5211313e+00  1.1308872e+00  1.4944680e+00  4.4294488e-01\n",
      "   6.8370253e-01  8.7285244e-01  1.3589848e+00  4.4169165e-03\n",
      "   1.2034194e+00  1.3296769e+00  8.4544110e-01]\n",
      " [ 2.4364259e+00  2.0523272e+00  1.6835430e+00  5.3037400e+00\n",
      "   1.3125927e+00  7.8371346e-01  1.4504147e+00  2.4752331e-01\n",
      "   2.6308990e+00  2.4366167e+00  1.5160786e+00  4.6210814e-01\n",
      "   1.9032485e+00  2.2662228e-01  8.3654165e-01  9.5798874e-01\n",
      "   8.7550390e-01  7.5000119e-01  3.6673207e+00  2.1057622e+00\n",
      "   3.5960069e+00  3.4243474e+00  1.5540123e+00  2.4251449e-01\n",
      "   6.3300246e-01  1.6442306e+00  2.8647141e+00  1.2880894e+00\n",
      "   2.4749427e+00  1.5424591e+00  1.0300190e+00  8.2056355e-01\n",
      "   2.1078897e+00  2.8550019e+00  6.5552831e-01  5.0886953e-01\n",
      "   1.2040358e+00  8.4104049e-01  1.0941272e+00  6.3241476e-01\n",
      "   5.2079791e-01  1.8613653e+00  1.2230424e+00  5.9420633e-01\n",
      "   1.3616670e+00  1.4686421e+00  1.3016778e+00  9.5763522e-01\n",
      "   1.4120363e+00  1.4665873e+00  2.0701699e+00  2.3480239e+00\n",
      "   1.9403290e+00  1.7498597e+00  1.0214653e+00  8.0616605e-01\n",
      "   9.6657252e-01  1.5207455e+00  1.1298516e+00  1.2281585e+00\n",
      "   9.0786886e-01  1.4653938e+00  1.1365563e+00  1.0058534e+00\n",
      "   6.2681174e-01  2.6024616e+00  1.6055036e+00  9.1184306e-01\n",
      "   2.9098465e+00  1.0748864e+00  1.0853603e+00  9.7512186e-01\n",
      "   9.5600927e-01  7.8609306e-01  1.0688444e+00  1.4346052e+00\n",
      "   1.3758786e+00  7.4996209e-01  8.2421213e-01  1.2355754e+00\n",
      "   3.7033522e+00  1.1284075e+00  1.2700679e+00  5.2994984e-01\n",
      "   6.4285165e-01  9.8896116e-01  1.5236486e+00  1.5986349e-02\n",
      "   1.1236377e+00  1.3442347e+00  8.5206771e-01]\n",
      " [ 2.4405115e+00  2.0674205e+00  1.5831552e+00  5.0247755e+00\n",
      "   1.2536089e+00  7.8940052e-01  1.3929880e+00  2.7328724e-01\n",
      "   2.6178613e+00  2.3734417e+00  1.5731649e+00  5.1689011e-01\n",
      "   1.8762555e+00  1.9168353e-01  7.7708316e-01  8.8698918e-01\n",
      "   8.3705246e-01  7.8358817e-01  3.5829644e+00  2.0055718e+00\n",
      "   3.4107842e+00  3.3082316e+00  1.5007069e+00  2.6228288e-01\n",
      "   6.2803471e-01  1.6671109e+00  2.6845579e+00  1.1974797e+00\n",
      "   2.3554351e+00  1.4638371e+00  9.8740298e-01  7.7199036e-01\n",
      "   2.0687392e+00  2.7968633e+00  6.4950937e-01  4.5978612e-01\n",
      "   1.1772723e+00  7.4890691e-01  9.7699380e-01  6.1358064e-01\n",
      "   5.5712193e-01  1.8223960e+00  1.1599151e+00  5.6671655e-01\n",
      "   1.2922618e+00  1.4341383e+00  1.2911456e+00  1.0180832e+00\n",
      "   1.3295513e+00  1.4113222e+00  1.9413570e+00  2.2335067e+00\n",
      "   1.9185236e+00  1.6250373e+00  9.6459687e-01  7.7190077e-01\n",
      "   8.9399731e-01  1.4638286e+00  1.0107642e+00  1.2080376e+00\n",
      "   9.1736549e-01  1.3731520e+00  1.1711687e+00  9.6832031e-01\n",
      "   5.4858321e-01  2.4895866e+00  1.5892655e+00  9.0747947e-01\n",
      "   2.8412249e+00  1.0503732e+00  1.0639098e+00  9.3098605e-01\n",
      "   9.4038570e-01  8.0022740e-01  9.7945386e-01  1.4245260e+00\n",
      "   1.3871881e+00  7.0088232e-01  7.8952557e-01  1.1921949e+00\n",
      "   3.6496501e+00  1.0757169e+00  1.1965671e+00  5.6894809e-01\n",
      "   6.0371029e-01  9.9014258e-01  1.4679571e+00  1.2942525e-02\n",
      "   9.8328406e-01  1.3421758e+00  8.4528512e-01]\n",
      " [ 2.4788885e+00  2.0653079e+00  1.5436101e+00  4.9300885e+00\n",
      "   1.2287712e+00  8.0783808e-01  1.3777772e+00  2.8306592e-01\n",
      "   2.6440325e+00  2.3257265e+00  1.6130930e+00  5.5145943e-01\n",
      "   1.8626485e+00  1.7817710e-01  7.4702621e-01  8.7354660e-01\n",
      "   8.2614708e-01  7.7981865e-01  3.5376186e+00  1.9520935e+00\n",
      "   3.3046336e+00  3.2256653e+00  1.4906539e+00  2.7183396e-01\n",
      "   6.2619883e-01  1.6828290e+00  2.5800757e+00  1.1534524e+00\n",
      "   2.2953627e+00  1.4174058e+00  9.7056764e-01  7.5439012e-01\n",
      "   2.0777519e+00  2.7585096e+00  6.4161742e-01  4.3214583e-01\n",
      "   1.1874595e+00  7.0506477e-01  9.4579750e-01  5.9880996e-01\n",
      "   5.6863153e-01  1.8110530e+00  1.0986749e+00  5.5947179e-01\n",
      "   1.2788787e+00  1.4226704e+00  1.2853832e+00  1.0295348e+00\n",
      "   1.2964821e+00  1.3891625e+00  1.8766049e+00  2.1705294e+00\n",
      "   1.9048158e+00  1.5898041e+00  9.3873209e-01  7.6379520e-01\n",
      "   8.6381203e-01  1.4567068e+00  9.4530112e-01  1.1837616e+00\n",
      "   8.9733791e-01  1.3066740e+00  1.1789064e+00  9.6154189e-01\n",
      "   5.0351095e-01  2.4355483e+00  1.5824342e+00  8.8258135e-01\n",
      "   2.7997518e+00  1.0433987e+00  1.0430862e+00  9.0113580e-01\n",
      "   9.2116928e-01  8.0150723e-01  9.3383479e-01  1.4045960e+00\n",
      "   1.3861678e+00  6.8924618e-01  7.6141179e-01  1.1765888e+00\n",
      "   3.6222391e+00  1.0558801e+00  1.1564747e+00  5.7251042e-01\n",
      "   5.8159584e-01  1.0024579e+00  1.4472313e+00  1.7911188e-02\n",
      "   9.2211032e-01  1.3350705e+00  8.2209623e-01]\n",
      " [ 2.5288110e+00  2.0630805e+00  1.5115708e+00  4.8573656e+00\n",
      "   1.2115290e+00  8.3345425e-01  1.3619821e+00  2.8844783e-01\n",
      "   2.6595857e+00  2.3039017e+00  1.6374822e+00  5.7263947e-01\n",
      "   1.8516748e+00  1.6425818e-01  7.2710609e-01  8.6307418e-01\n",
      "   8.1439543e-01  7.8326344e-01  3.5117674e+00  1.9009566e+00\n",
      "   3.2357569e+00  3.1529789e+00  1.4680543e+00  2.7849761e-01\n",
      "   6.2622714e-01  1.6978881e+00  2.4927981e+00  1.1206064e+00\n",
      "   2.2529609e+00  1.3869927e+00  9.6317685e-01  7.4399889e-01\n",
      "   2.0889561e+00  2.7390831e+00  6.2851888e-01  4.1103521e-01\n",
      "   1.1963950e+00  6.7288452e-01  9.2701566e-01  5.8276087e-01\n",
      "   5.8276057e-01  1.8270682e+00  1.0420930e+00  5.5059028e-01\n",
      "   1.2687509e+00  1.4081421e+00  1.2809510e+00  1.0372820e+00\n",
      "   1.2656906e+00  1.3786674e+00  1.8356799e+00  2.1126399e+00\n",
      "   1.8967882e+00  1.5610363e+00  9.1684949e-01  7.5880229e-01\n",
      "   8.4429932e-01  1.4492949e+00  8.8665617e-01  1.1760145e+00\n",
      "   8.8663220e-01  1.2583261e+00  1.1898782e+00  9.5201284e-01\n",
      "   4.6972567e-01  2.3953693e+00  1.5763083e+00  8.5554689e-01\n",
      "   2.7704387e+00  1.0410848e+00  1.0255169e+00  8.7782645e-01\n",
      "   9.1405845e-01  8.0429602e-01  8.9349258e-01  1.3829095e+00\n",
      "   1.3755761e+00  6.7688215e-01  7.2862262e-01  1.1603706e+00\n",
      "   3.6106491e+00  1.0492219e+00  1.1196994e+00  5.7771105e-01\n",
      "   5.6230557e-01  1.0095334e+00  1.4451662e+00  2.5575433e-02\n",
      "   8.6463940e-01  1.3356533e+00  7.9889309e-01]\n",
      " [ 2.6101403e+00  2.0544076e+00  1.4704823e+00  4.7703071e+00\n",
      "   1.1766288e+00  8.7701315e-01  1.3186767e+00  2.8975722e-01\n",
      "   2.6417754e+00  2.2937701e+00  1.6507099e+00  6.0052514e-01\n",
      "   1.8195245e+00  1.4888045e-01  7.1446908e-01  8.3993387e-01\n",
      "   8.1633973e-01  7.5437224e-01  3.4297500e+00  1.8090194e+00\n",
      "   3.1382117e+00  3.0186317e+00  1.4382646e+00  2.8449944e-01\n",
      "   6.2321275e-01  1.7190289e+00  2.3529644e+00  1.0809343e+00\n",
      "   2.1980896e+00  1.3461523e+00  9.4633150e-01  7.2825873e-01\n",
      "   2.0729258e+00  2.6874735e+00  6.0234690e-01  3.8019040e-01\n",
      "   1.1990280e+00  6.1847317e-01  9.1260201e-01  5.5421662e-01\n",
      "   6.0358173e-01  1.8296279e+00  9.5613360e-01  5.4298317e-01\n",
      "   1.2504907e+00  1.3844032e+00  1.2574775e+00  1.0269829e+00\n",
      "   1.1864252e+00  1.3854988e+00  1.7870908e+00  1.9905684e+00\n",
      "   1.8768432e+00  1.5222189e+00  8.7722886e-01  7.5614238e-01\n",
      "   8.2287145e-01  1.4498479e+00  7.9822791e-01  1.1691890e+00\n",
      "   8.8297784e-01  1.1936958e+00  1.1998111e+00  9.2952859e-01\n",
      "   4.1595525e-01  2.3488283e+00  1.5472653e+00  8.0315334e-01\n",
      "   2.7280304e+00  1.0472382e+00  1.0013692e+00  8.4353435e-01\n",
      "   9.0998363e-01  7.9770052e-01  8.5686815e-01  1.3382245e+00\n",
      "   1.3313082e+00  6.6194922e-01  6.6763842e-01  1.1293890e+00\n",
      "   3.6023366e+00  1.0509484e+00  1.0728984e+00  5.8546573e-01\n",
      "   5.1819444e-01  9.9441898e-01  1.4486881e+00  3.8535036e-02\n",
      "   7.9044652e-01  1.3466969e+00  7.5055057e-01]]\n"
     ]
    }
   ],
   "source": [
    "# Predict for the next 7 days\n",
    "test_predictions = predict_next_7_days(loaded_model, scaled_data, sequence_length, scaler)\n",
    "\n",
    "print(\"Predictions for the next 7 days:\", test_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e0d33f9c-e1a2-44d5-90cd-f57a29dff885",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Round predictions to nearest integer\n",
    "rounded_predictions = np.round(test_predictions).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "97bb461e-37cd-4ed5-a7e5-63678874a6cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 2, 2, 6, 1, 2, 1, 0, 2, 3, 1, 0, 2, 0, 1, 1, 1, 1, 4, 2, 4, 3,\n",
       "        2, 1, 1, 2, 4, 1, 3, 2, 1, 1, 2, 4, 0, 0, 1, 1, 1, 1, 0, 3, 2, 1,\n",
       "        1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 3,\n",
       "        1, 1, 4, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 5, 1, 2, 1, 1, 1, 2, 0,\n",
       "        1, 2, 1],\n",
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       "        2, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 0,\n",
       "        1, 1, 1],\n",
       "       [2, 2, 2, 5, 1, 1, 1, 0, 3, 2, 2, 1, 2, 0, 1, 1, 1, 1, 4, 2, 3, 3,\n",
       "        1, 0, 1, 2, 3, 1, 2, 1, 1, 1, 2, 3, 1, 0, 1, 1, 1, 1, 1, 2, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2,\n",
       "        2, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 0,\n",
       "        1, 1, 1],\n",
       "       [3, 2, 2, 5, 1, 1, 1, 0, 3, 2, 2, 1, 2, 0, 1, 1, 1, 1, 4, 2, 3, 3,\n",
       "        1, 0, 1, 2, 2, 1, 2, 1, 1, 1, 2, 3, 1, 0, 1, 1, 1, 1, 1, 2, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 2,\n",
       "        2, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 0,\n",
       "        1, 1, 1],\n",
       "       [3, 2, 1, 5, 1, 1, 1, 0, 3, 2, 2, 1, 2, 0, 1, 1, 1, 1, 3, 2, 3, 3,\n",
       "        1, 0, 1, 2, 2, 1, 2, 1, 1, 1, 2, 3, 1, 0, 1, 1, 1, 1, 1, 2, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 2,\n",
       "        2, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 0,\n",
       "        1, 1, 1]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rounded_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f8dff74f-0090-4a7b-96bb-dd36f9be333c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we got the 7 day predictions we need to convert to the show pizza_name\n",
    "# And then calculate quantity of ingredients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c9bf1869-f8e9-4d3c-808e-c36f7cf4871a",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bbq_ckn_l</th>\n",
       "      <th>bbq_ckn_m</th>\n",
       "      <th>bbq_ckn_s</th>\n",
       "      <th>big_meat_s</th>\n",
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       "      <th>calabrese_m</th>\n",
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       "      <th>cali_ckn_m</th>\n",
       "      <th>...</th>\n",
       "      <th>thai_ckn_m</th>\n",
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       "      <th>the_greek_l</th>\n",
       "      <th>the_greek_m</th>\n",
       "      <th>the_greek_s</th>\n",
       "      <th>the_greek_xl</th>\n",
       "      <th>the_greek_xxl</th>\n",
       "      <th>veggie_veg_l</th>\n",
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       "      <th>veggie_veg_s</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-03</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
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       "      <th>2015-01-04</th>\n",
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       "      <td>2</td>\n",
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       "      <td>3</td>\n",
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      ],
      "text/plain": [
       "            bbq_ckn_l  bbq_ckn_m  bbq_ckn_s  big_meat_s  brie_carre_s  \\\n",
       "order_date                                                              \n",
       "2015-01-01          6          4          1           5             0   \n",
       "2015-01-02          4          7          1           9             3   \n",
       "2015-01-03          2          0          0           3             1   \n",
       "2015-01-04          0          2          0           3             0   \n",
       "2015-01-05          2          2          2           1             1   \n",
       "\n",
       "            calabrese_l  calabrese_m  calabrese_s  cali_ckn_l  cali_ckn_m  \\\n",
       "order_date                                                                  \n",
       "2015-01-01            0            1            0           2           1   \n",
       "2015-01-02            2            5            0           4           9   \n",
       "2015-01-03            0            1            0           1           4   \n",
       "2015-01-04            1            1            0           2           1   \n",
       "2015-01-05            1            2            1           5           5   \n",
       "\n",
       "            ...  thai_ckn_m  thai_ckn_s  the_greek_l  the_greek_m  \\\n",
       "order_date  ...                                                     \n",
       "2015-01-01  ...           2           0            0            0   \n",
       "2015-01-02  ...           1           1            1            1   \n",
       "2015-01-03  ...           0           2            0            1   \n",
       "2015-01-04  ...           1           0            0            2   \n",
       "2015-01-05  ...           3           2            1            2   \n",
       "\n",
       "            the_greek_s  the_greek_xl  the_greek_xxl  veggie_veg_l  \\\n",
       "order_date                                                           \n",
       "2015-01-01            4             1              0             1   \n",
       "2015-01-02            0             2              0             1   \n",
       "2015-01-03            1             1              0             3   \n",
       "2015-01-04            2             2              0             0   \n",
       "2015-01-05            1             1              0             2   \n",
       "\n",
       "            veggie_veg_m  veggie_veg_s  \n",
       "order_date                              \n",
       "2015-01-01             0             2  \n",
       "2015-01-02             0             0  \n",
       "2015-01-03             1             2  \n",
       "2015-01-04             2             3  \n",
       "2015-01-05             2             2  \n",
       "\n",
       "[5 rows x 91 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "66072c55-050c-48db-9cfe-47b2c44102c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['bbq_ckn_l', 'bbq_ckn_m', 'bbq_ckn_s', 'big_meat_s', 'brie_carre_s',\n",
       "       'calabrese_l', 'calabrese_m', 'calabrese_s', 'cali_ckn_l', 'cali_ckn_m',\n",
       "       'cali_ckn_s', 'ckn_alfredo_l', 'ckn_alfredo_m', 'ckn_alfredo_s',\n",
       "       'ckn_pesto_l', 'ckn_pesto_m', 'ckn_pesto_s', 'classic_dlx_l',\n",
       "       'classic_dlx_m', 'classic_dlx_s', 'five_cheese_l', 'four_cheese_l',\n",
       "       'four_cheese_m', 'green_garden_l', 'green_garden_m', 'green_garden_s',\n",
       "       'hawaiian_l', 'hawaiian_m', 'hawaiian_s', 'ital_cpcllo_l',\n",
       "       'ital_cpcllo_m', 'ital_cpcllo_s', 'ital_supr_l', 'ital_supr_m',\n",
       "       'ital_supr_s', 'ital_veggie_l', 'ital_veggie_m', 'ital_veggie_s',\n",
       "       'mediterraneo_l', 'mediterraneo_m', 'mediterraneo_s', 'mexicana_l',\n",
       "       'mexicana_m', 'mexicana_s', 'napolitana_l', 'napolitana_m',\n",
       "       'napolitana_s', 'pep_msh_pep_l', 'pep_msh_pep_m', 'pep_msh_pep_s',\n",
       "       'pepperoni_l', 'pepperoni_m', 'pepperoni_s', 'peppr_salami_l',\n",
       "       'peppr_salami_m', 'peppr_salami_s', 'prsc_argla_l', 'prsc_argla_m',\n",
       "       'prsc_argla_s', 'sicilian_l', 'sicilian_m', 'sicilian_s',\n",
       "       'soppressata_l', 'soppressata_m', 'soppressata_s', 'southw_ckn_l',\n",
       "       'southw_ckn_m', 'southw_ckn_s', 'spicy_ital_l', 'spicy_ital_m',\n",
       "       'spicy_ital_s', 'spin_pesto_l', 'spin_pesto_m', 'spin_pesto_s',\n",
       "       'spinach_fet_l', 'spinach_fet_m', 'spinach_fet_s', 'spinach_supr_l',\n",
       "       'spinach_supr_m', 'spinach_supr_s', 'thai_ckn_l', 'thai_ckn_m',\n",
       "       'thai_ckn_s', 'the_greek_l', 'the_greek_m', 'the_greek_s',\n",
       "       'the_greek_xl', 'the_greek_xxl', 'veggie_veg_l', 'veggie_veg_m',\n",
       "       'veggie_veg_s'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pizza_names = data.columns\n",
    "pizza_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ac62dc08-33ef-495c-8378-071f8522d19a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_forecasted = pd.DataFrame(rounded_predictions, columns=pizza_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "d997649f-8cc2-4649-8d19-f06acb7aad06",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>bbq_ckn_l</th>\n",
       "      <th>bbq_ckn_m</th>\n",
       "      <th>bbq_ckn_s</th>\n",
       "      <th>big_meat_s</th>\n",
       "      <th>brie_carre_s</th>\n",
       "      <th>calabrese_l</th>\n",
       "      <th>calabrese_m</th>\n",
       "      <th>calabrese_s</th>\n",
       "      <th>cali_ckn_l</th>\n",
       "      <th>cali_ckn_m</th>\n",
       "      <th>...</th>\n",
       "      <th>thai_ckn_m</th>\n",
       "      <th>thai_ckn_s</th>\n",
       "      <th>the_greek_l</th>\n",
       "      <th>the_greek_m</th>\n",
       "      <th>the_greek_s</th>\n",
       "      <th>the_greek_xl</th>\n",
       "      <th>the_greek_xxl</th>\n",
       "      <th>veggie_veg_l</th>\n",
       "      <th>veggie_veg_m</th>\n",
       "      <th>veggie_veg_s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 91 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   bbq_ckn_l  bbq_ckn_m  bbq_ckn_s  big_meat_s  brie_carre_s  calabrese_l  \\\n",
       "0          3          2          2           6             1            2   \n",
       "1          2          2          2           5             1            1   \n",
       "2          2          2          2           5             1            1   \n",
       "3          2          2          2           5             1            1   \n",
       "4          2          2          2           5             1            1   \n",
       "5          3          2          2           5             1            1   \n",
       "6          3          2          1           5             1            1   \n",
       "\n",
       "   calabrese_m  calabrese_s  cali_ckn_l  cali_ckn_m  ...  thai_ckn_m  \\\n",
       "0            1            0           2           3  ...           1   \n",
       "1            1            0           2           2  ...           1   \n",
       "2            1            0           3           2  ...           1   \n",
       "3            1            0           3           2  ...           1   \n",
       "4            1            0           3           2  ...           1   \n",
       "5            1            0           3           2  ...           1   \n",
       "6            1            0           3           2  ...           1   \n",
       "\n",
       "   thai_ckn_s  the_greek_l  the_greek_m  the_greek_s  the_greek_xl  \\\n",
       "0           2            1            1            1             2   \n",
       "1           1            0            1            1             1   \n",
       "2           1            1            1            1             2   \n",
       "3           1            1            1            1             1   \n",
       "4           1            1            1            1             1   \n",
       "5           1            1            1            1             1   \n",
       "6           1            1            1            1             1   \n",
       "\n",
       "   the_greek_xxl  veggie_veg_l  veggie_veg_m  veggie_veg_s  \n",
       "0              0             1             2             1  \n",
       "1              0             1             1             1  \n",
       "2              0             1             1             1  \n",
       "3              0             1             1             1  \n",
       "4              0             1             1             1  \n",
       "5              0             1             1             1  \n",
       "6              0             1             1             1  \n",
       "\n",
       "[7 rows x 91 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_forecasted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "aa8fc8e5-3e75-48c3-9039-455ec18c8c9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2015-12-31 00:00:00')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_date = data.index.max()\n",
    "max_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "bd41a30a-40f1-493f-91bd-565f5dd674bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "next_1_day = max_date + pd.Timedelta(days=1)\n",
    "next_7_days = max_date + pd.Timedelta(days=7)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "13f36c0f-4b2c-4b17-a75d-2ab35632b4a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2016-01-07 00:00:00')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(next_1_day)\n",
    "print(next_7_days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "9f4da13b-9416-4917-8ff0-21990fe05d4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name = f\"{next_1_day.strftime('%b%d')}_to_{next_7_days.strftime('%b%d')}_forecast.csv\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "9c981823-a7d6-44e6-b975-2fd4fc21b46c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File saved as: Jan01_to_Jan07_forecast.csv\n"
     ]
    }
   ],
   "source": [
    "df_forecasted.to_csv(file_name, index=False)\n",
    "print(f\"File saved as: {file_name}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "942106cd-b43c-4ff7-b02b-89435b2cab47",
   "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>bbq_ckn_l</th>\n",
       "      <th>bbq_ckn_m</th>\n",
       "      <th>bbq_ckn_s</th>\n",
       "      <th>big_meat_s</th>\n",
       "      <th>brie_carre_s</th>\n",
       "      <th>calabrese_l</th>\n",
       "      <th>calabrese_m</th>\n",
       "      <th>calabrese_s</th>\n",
       "      <th>cali_ckn_l</th>\n",
       "      <th>cali_ckn_m</th>\n",
       "      <th>...</th>\n",
       "      <th>thai_ckn_m</th>\n",
       "      <th>thai_ckn_s</th>\n",
       "      <th>the_greek_l</th>\n",
       "      <th>the_greek_m</th>\n",
       "      <th>the_greek_s</th>\n",
       "      <th>the_greek_xl</th>\n",
       "      <th>the_greek_xxl</th>\n",
       "      <th>veggie_veg_l</th>\n",
       "      <th>veggie_veg_m</th>\n",
       "      <th>veggie_veg_s</th>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>...</td>\n",
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       "      <th>2</th>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>5</th>\n",
       "      <td>3</td>\n",
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       "      <td>5</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 91 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   bbq_ckn_l  bbq_ckn_m  bbq_ckn_s  big_meat_s  brie_carre_s  calabrese_l  \\\n",
       "0          3          2          2           6             1            2   \n",
       "1          2          2          2           5             1            1   \n",
       "2          2          2          2           5             1            1   \n",
       "3          2          2          2           5             1            1   \n",
       "4          2          2          2           5             1            1   \n",
       "5          3          2          2           5             1            1   \n",
       "6          3          2          1           5             1            1   \n",
       "\n",
       "   calabrese_m  calabrese_s  cali_ckn_l  cali_ckn_m  ...  thai_ckn_m  \\\n",
       "0            1            0           2           3  ...           1   \n",
       "1            1            0           2           2  ...           1   \n",
       "2            1            0           3           2  ...           1   \n",
       "3            1            0           3           2  ...           1   \n",
       "4            1            0           3           2  ...           1   \n",
       "5            1            0           3           2  ...           1   \n",
       "6            1            0           3           2  ...           1   \n",
       "\n",
       "   thai_ckn_s  the_greek_l  the_greek_m  the_greek_s  the_greek_xl  \\\n",
       "0           2            1            1            1             2   \n",
       "1           1            0            1            1             1   \n",
       "2           1            1            1            1             2   \n",
       "3           1            1            1            1             1   \n",
       "4           1            1            1            1             1   \n",
       "5           1            1            1            1             1   \n",
       "6           1            1            1            1             1   \n",
       "\n",
       "   the_greek_xxl  veggie_veg_l  veggie_veg_m  veggie_veg_s  \n",
       "0              0             1             2             1  \n",
       "1              0             1             1             1  \n",
       "2              0             1             1             1  \n",
       "3              0             1             1             1  \n",
       "4              0             1             1             1  \n",
       "5              0             1             1             1  \n",
       "6              0             1             1             1  \n",
       "\n",
       "[7 rows x 91 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_forecast = pd.read_csv(file_name)\n",
    "df_forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "8646a4b1-29fe-4953-b84e-b0065ac4ec03",
   "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>pizza_name_id</th>\n",
       "      <th>pizza_name</th>\n",
       "      <th>pizza_ingredients</th>\n",
       "      <th>Items_Qty_In_Grams</th>\n",
       "      <th>pizza_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bbq_ckn_l</td>\n",
       "      <td>The Barbecue Chicken Pizza</td>\n",
       "      <td>Barbecued Chicken</td>\n",
       "      <td>40</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bbq_ckn_l</td>\n",
       "      <td>The Barbecue Chicken Pizza</td>\n",
       "      <td>Red Peppers</td>\n",
       "      <td>15</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bbq_ckn_l</td>\n",
       "      <td>The Barbecue Chicken Pizza</td>\n",
       "      <td>Green Peppers</td>\n",
       "      <td>20</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bbq_ckn_l</td>\n",
       "      <td>The Barbecue Chicken Pizza</td>\n",
       "      <td>Tomatoes</td>\n",
       "      <td>30</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bbq_ckn_l</td>\n",
       "      <td>The Barbecue Chicken Pizza</td>\n",
       "      <td>Red Onions</td>\n",
       "      <td>60</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  pizza_name_id                  pizza_name  pizza_ingredients  \\\n",
       "0     bbq_ckn_l  The Barbecue Chicken Pizza  Barbecued Chicken   \n",
       "1     bbq_ckn_l  The Barbecue Chicken Pizza        Red Peppers   \n",
       "2     bbq_ckn_l  The Barbecue Chicken Pizza      Green Peppers   \n",
       "3     bbq_ckn_l  The Barbecue Chicken Pizza           Tomatoes   \n",
       "4     bbq_ckn_l  The Barbecue Chicken Pizza         Red Onions   \n",
       "\n",
       "   Items_Qty_In_Grams pizza_size  \n",
       "0                  40          L  \n",
       "1                  15          L  \n",
       "2                  20          L  \n",
       "3                  30          L  \n",
       "4                  60          L  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's also create order with pizzas in each day\n",
    "#and then also quantity in grams of each ingredient \n",
    "df_ing = pd.read_excel('Pizza_ingredients_cleaned.xlsx')\n",
    "df_ing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "6f59d6c4-df52-4b90-a225-6f76d30b8d2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Pizza Quantity Forecast for Day 1:\n",
      "\n",
      "          Code                                 Pizza Name Size  Quantity\n",
      "     bbq_ckn_l                 The Barbecue Chicken Pizza    L         3\n",
      "     bbq_ckn_m                 The Barbecue Chicken Pizza    M         2\n",
      "     bbq_ckn_s                 The Barbecue Chicken Pizza    S         2\n",
      "    big_meat_s                         The Big Meat Pizza    S         6\n",
      "  brie_carre_s                       The Brie Carre Pizza    S         1\n",
      "   calabrese_l                        The Calabrese Pizza    L         2\n",
      "   calabrese_m                        The Calabrese Pizza    M         1\n",
      "    cali_ckn_l               The California Chicken Pizza    L         2\n",
      "    cali_ckn_m               The California Chicken Pizza    M         3\n",
      "    cali_ckn_s               The California Chicken Pizza    S         1\n",
      " ckn_alfredo_m                  The Chicken Alfredo Pizza    M         2\n",
      "   ckn_pesto_l                    The Chicken Pesto Pizza    L         1\n",
      "   ckn_pesto_m                    The Chicken Pesto Pizza    M         1\n",
      "   ckn_pesto_s                    The Chicken Pesto Pizza    S         1\n",
      " classic_dlx_l                   The Classic Deluxe Pizza    L         1\n",
      " classic_dlx_m                   The Classic Deluxe Pizza    M         4\n",
      " classic_dlx_s                   The Classic Deluxe Pizza    S         2\n",
      " five_cheese_l                      The Five Cheese Pizza    L         4\n",
      " four_cheese_l                      The Four Cheese Pizza    L         3\n",
      " four_cheese_m                      The Four Cheese Pizza    M         2\n",
      "green_garden_l                     The Green Garden Pizza    L         1\n",
      "green_garden_m                     The Green Garden Pizza    M         1\n",
      "green_garden_s                     The Green Garden Pizza    S         2\n",
      "    hawaiian_l                         The Hawaiian Pizza    L         4\n",
      "    hawaiian_m                         The Hawaiian Pizza    M         1\n",
      "    hawaiian_s                         The Hawaiian Pizza    S         3\n",
      " ital_cpcllo_l                The Italian Capocollo Pizza    L         2\n",
      " ital_cpcllo_m                The Italian Capocollo Pizza    M         1\n",
      " ital_cpcllo_s                The Italian Capocollo Pizza    S         1\n",
      "   ital_supr_l                  The Italian Supreme Pizza    L         2\n",
      "   ital_supr_m                  The Italian Supreme Pizza    M         4\n",
      " ital_veggie_m               The Italian Vegetables Pizza    M         1\n",
      " ital_veggie_s               The Italian Vegetables Pizza    S         1\n",
      "mediterraneo_l                    The Mediterranean Pizza    L         1\n",
      "mediterraneo_m                    The Mediterranean Pizza    M         1\n",
      "    mexicana_l                         The Mexicana Pizza    L         3\n",
      "    mexicana_m                         The Mexicana Pizza    M         2\n",
      "    mexicana_s                         The Mexicana Pizza    S         1\n",
      "  napolitana_l                       The Napolitana Pizza    L         1\n",
      "  napolitana_m                       The Napolitana Pizza    M         2\n",
      "  napolitana_s                       The Napolitana Pizza    S         2\n",
      " pep_msh_pep_l The Pepperoni, Mushroom, and Peppers Pizza    L         1\n",
      " pep_msh_pep_m The Pepperoni, Mushroom, and Peppers Pizza    M         2\n",
      " pep_msh_pep_s The Pepperoni, Mushroom, and Peppers Pizza    S         2\n",
      "   pepperoni_l                        The Pepperoni Pizza    L         2\n",
      "   pepperoni_m                        The Pepperoni Pizza    M         2\n",
      "   pepperoni_s                        The Pepperoni Pizza    S         2\n",
      "peppr_salami_l                    The Pepper Salami Pizza    L         2\n",
      "peppr_salami_m                    The Pepper Salami Pizza    M         1\n",
      "peppr_salami_s                    The Pepper Salami Pizza    S         1\n",
      "  prsc_argla_l           The Prosciutto and Arugula Pizza    L         1\n",
      "  prsc_argla_m           The Prosciutto and Arugula Pizza    M         2\n",
      "  prsc_argla_s           The Prosciutto and Arugula Pizza    S         1\n",
      "    sicilian_l                         The Sicilian Pizza    L         2\n",
      "    sicilian_m                         The Sicilian Pizza    M         1\n",
      "    sicilian_s                         The Sicilian Pizza    S         1\n",
      " soppressata_l                      The Soppressata Pizza    L         1\n",
      " soppressata_m                      The Soppressata Pizza    M         1\n",
      " soppressata_s                      The Soppressata Pizza    S         1\n",
      "  southw_ckn_l                The Southwest Chicken Pizza    L         3\n",
      "  southw_ckn_m                The Southwest Chicken Pizza    M         1\n",
      "  southw_ckn_s                The Southwest Chicken Pizza    S         1\n",
      "  spicy_ital_l                    The Spicy Italian Pizza    L         4\n",
      "  spicy_ital_m                    The Spicy Italian Pizza    M         1\n",
      "  spicy_ital_s                    The Spicy Italian Pizza    S         2\n",
      "  spin_pesto_l                    The Spinach Pesto Pizza    L         1\n",
      "  spin_pesto_m                    The Spinach Pesto Pizza    M         1\n",
      "  spin_pesto_s                    The Spinach Pesto Pizza    S         1\n",
      " spinach_fet_l                 The Spinach and Feta Pizza    L         1\n",
      " spinach_fet_m                 The Spinach and Feta Pizza    M         1\n",
      " spinach_fet_s                 The Spinach and Feta Pizza    S         2\n",
      "spinach_supr_l                  The Spinach Supreme Pizza    L         1\n",
      "spinach_supr_m                  The Spinach Supreme Pizza    M         1\n",
      "spinach_supr_s                  The Spinach Supreme Pizza    S         1\n",
      "    thai_ckn_l                     The Thai Chicken Pizza    L         5\n",
      "    thai_ckn_m                     The Thai Chicken Pizza    M         1\n",
      "    thai_ckn_s                     The Thai Chicken Pizza    S         2\n",
      "   the_greek_l                            The Greek Pizza    L         1\n",
      "   the_greek_m                            The Greek Pizza    M         1\n",
      "   the_greek_s                            The Greek Pizza    S         1\n",
      "  the_greek_xl                            The Greek Pizza    L         2\n",
      "  veggie_veg_l          The Vegetables + Vegetables Pizza    L         1\n",
      "  veggie_veg_m          The Vegetables + Vegetables Pizza    M         2\n",
      "  veggie_veg_s          The Vegetables + Vegetables Pizza    S         1\n",
      "\n",
      "Pizza Quantity Forecast for Day 2:\n",
      "\n",
      "          Code                                 Pizza Name Size  Quantity\n",
      "     bbq_ckn_l                 The Barbecue Chicken Pizza    L         2\n",
      "     bbq_ckn_m                 The Barbecue Chicken Pizza    M         2\n",
      "     bbq_ckn_s                 The Barbecue Chicken Pizza    S         2\n",
      "    big_meat_s                         The Big Meat Pizza    S         5\n",
      "  brie_carre_s                       The Brie Carre Pizza    S         1\n",
      "   calabrese_l                        The Calabrese Pizza    L         1\n",
      "   calabrese_m                        The Calabrese Pizza    M         1\n",
      "    cali_ckn_l               The California Chicken Pizza    L         2\n",
      "    cali_ckn_m               The California Chicken Pizza    M         2\n",
      "    cali_ckn_s               The California Chicken Pizza    S         1\n",
      " ckn_alfredo_m                  The Chicken Alfredo Pizza    M         2\n",
      "   ckn_pesto_l                    The Chicken Pesto Pizza    L         1\n",
      "   ckn_pesto_m                    The Chicken Pesto Pizza    M         1\n",
      "   ckn_pesto_s                    The Chicken Pesto Pizza    S         1\n",
      " classic_dlx_l                   The Classic Deluxe Pizza    L         1\n",
      " classic_dlx_m                   The Classic Deluxe Pizza    M         3\n",
      " classic_dlx_s                   The Classic Deluxe Pizza    S         2\n",
      " five_cheese_l                      The Five Cheese Pizza    L         3\n",
      " four_cheese_l                      The Four Cheese Pizza    L         3\n",
      " four_cheese_m                      The Four Cheese Pizza    M         2\n",
      "green_garden_m                     The Green Garden Pizza    M         1\n",
      "green_garden_s                     The Green Garden Pizza    S         2\n",
      "    hawaiian_l                         The Hawaiian Pizza    L         3\n",
      "    hawaiian_m                         The Hawaiian Pizza    M         1\n",
      "    hawaiian_s                         The Hawaiian Pizza    S         3\n",
      " ital_cpcllo_l                The Italian Capocollo Pizza    L         2\n",
      " ital_cpcllo_m                The Italian Capocollo Pizza    M         1\n",
      " ital_cpcllo_s                The Italian Capocollo Pizza    S         1\n",
      "   ital_supr_l                  The Italian Supreme Pizza    L         2\n",
      "   ital_supr_m                  The Italian Supreme Pizza    M         3\n",
      "   ital_supr_s                  The Italian Supreme Pizza    S         1\n",
      " ital_veggie_l               The Italian Vegetables Pizza    L         1\n",
      " ital_veggie_m               The Italian Vegetables Pizza    M         1\n",
      " ital_veggie_s               The Italian Vegetables Pizza    S         1\n",
      "mediterraneo_l                    The Mediterranean Pizza    L         1\n",
      "mediterraneo_m                    The Mediterranean Pizza    M         1\n",
      "    mexicana_l                         The Mexicana Pizza    L         2\n",
      "    mexicana_m                         The Mexicana Pizza    M         1\n",
      "    mexicana_s                         The Mexicana Pizza    S         1\n",
      "  napolitana_l                       The Napolitana Pizza    L         1\n",
      "  napolitana_m                       The Napolitana Pizza    M         1\n",
      "  napolitana_s                       The Napolitana Pizza    S         1\n",
      " pep_msh_pep_l The Pepperoni, Mushroom, and Peppers Pizza    L         1\n",
      " pep_msh_pep_m The Pepperoni, Mushroom, and Peppers Pizza    M         1\n",
      " pep_msh_pep_s The Pepperoni, Mushroom, and Peppers Pizza    S         1\n",
      "   pepperoni_l                        The Pepperoni Pizza    L         2\n",
      "   pepperoni_m                        The Pepperoni Pizza    M         2\n",
      "   pepperoni_s                        The Pepperoni Pizza    S         2\n",
      "peppr_salami_l                    The Pepper Salami Pizza    L         2\n",
      "peppr_salami_m                    The Pepper Salami Pizza    M         1\n",
      "peppr_salami_s                    The Pepper Salami Pizza    S         1\n",
      "  prsc_argla_l           The Prosciutto and Arugula Pizza    L         1\n",
      "  prsc_argla_m           The Prosciutto and Arugula Pizza    M         1\n",
      "  prsc_argla_s           The Prosciutto and Arugula Pizza    S         1\n",
      "    sicilian_l                         The Sicilian Pizza    L         1\n",
      "    sicilian_m                         The Sicilian Pizza    M         1\n",
      "    sicilian_s                         The Sicilian Pizza    S         2\n",
      " soppressata_l                      The Soppressata Pizza    L         1\n",
      " soppressata_m                      The Soppressata Pizza    M         1\n",
      " soppressata_s                      The Soppressata Pizza    S         1\n",
      "  southw_ckn_l                The Southwest Chicken Pizza    L         3\n",
      "  southw_ckn_m                The Southwest Chicken Pizza    M         1\n",
      "  southw_ckn_s                The Southwest Chicken Pizza    S         1\n",
      "  spicy_ital_l                    The Spicy Italian Pizza    L         3\n",
      "  spicy_ital_m                    The Spicy Italian Pizza    M         1\n",
      "  spicy_ital_s                    The Spicy Italian Pizza    S         1\n",
      "  spin_pesto_l                    The Spinach Pesto Pizza    L         1\n",
      "  spin_pesto_m                    The Spinach Pesto Pizza    M         1\n",
      "  spin_pesto_s                    The Spinach Pesto Pizza    S         1\n",
      " spinach_fet_l                 The Spinach and Feta Pizza    L         1\n",
      " spinach_fet_m                 The Spinach and Feta Pizza    M         1\n",
      " spinach_fet_s                 The Spinach and Feta Pizza    S         1\n",
      "spinach_supr_l                  The Spinach Supreme Pizza    L         1\n",
      "spinach_supr_m                  The Spinach Supreme Pizza    M         1\n",
      "spinach_supr_s                  The Spinach Supreme Pizza    S         1\n",
      "    thai_ckn_l                     The Thai Chicken Pizza    L         4\n",
      "    thai_ckn_m                     The Thai Chicken Pizza    M         1\n",
      "    thai_ckn_s                     The Thai Chicken Pizza    S         1\n",
      "   the_greek_m                            The Greek Pizza    M         1\n",
      "   the_greek_s                            The Greek Pizza    S         1\n",
      "  the_greek_xl                            The Greek Pizza    L         1\n",
      "  veggie_veg_l          The Vegetables + Vegetables Pizza    L         1\n",
      "  veggie_veg_m          The Vegetables + Vegetables Pizza    M         1\n",
      "  veggie_veg_s          The Vegetables + Vegetables Pizza    S         1\n",
      "\n",
      "Pizza Quantity Forecast for Day 3:\n",
      "\n",
      "          Code                                 Pizza Name Size  Quantity\n",
      "     bbq_ckn_l                 The Barbecue Chicken Pizza    L         2\n",
      "     bbq_ckn_m                 The Barbecue Chicken Pizza    M         2\n",
      "     bbq_ckn_s                 The Barbecue Chicken Pizza    S         2\n",
      "    big_meat_s                         The Big Meat Pizza    S         5\n",
      "  brie_carre_s                       The Brie Carre Pizza    S         1\n",
      "   calabrese_l                        The Calabrese Pizza    L         1\n",
      "   calabrese_m                        The Calabrese Pizza    M         1\n",
      "    cali_ckn_l               The California Chicken Pizza    L         3\n",
      "    cali_ckn_m               The California Chicken Pizza    M         2\n",
      "    cali_ckn_s               The California Chicken Pizza    S         2\n",
      " ckn_alfredo_m                  The Chicken Alfredo Pizza    M         2\n",
      "   ckn_pesto_l                    The Chicken Pesto Pizza    L         1\n",
      "   ckn_pesto_m                    The Chicken Pesto Pizza    M         1\n",
      "   ckn_pesto_s                    The Chicken Pesto Pizza    S         1\n",
      " classic_dlx_l                   The Classic Deluxe Pizza    L         1\n",
      " classic_dlx_m                   The Classic Deluxe Pizza    M         4\n",
      " classic_dlx_s                   The Classic Deluxe Pizza    S         2\n",
      " five_cheese_l                      The Five Cheese Pizza    L         4\n",
      " four_cheese_l                      The Four Cheese Pizza    L         3\n",
      " four_cheese_m                      The Four Cheese Pizza    M         2\n",
      "green_garden_m                     The Green Garden Pizza    M         1\n",
      "green_garden_s                     The Green Garden Pizza    S         2\n",
      "    hawaiian_l                         The Hawaiian Pizza    L         3\n",
      "    hawaiian_m                         The Hawaiian Pizza    M         1\n",
      "    hawaiian_s                         The Hawaiian Pizza    S         2\n",
      " ital_cpcllo_l                The Italian Capocollo Pizza    L         2\n",
      " ital_cpcllo_m                The Italian Capocollo Pizza    M         1\n",
      " ital_cpcllo_s                The Italian Capocollo Pizza    S         1\n",
      "   ital_supr_l                  The Italian Supreme Pizza    L         2\n",
      "   ital_supr_m                  The Italian Supreme Pizza    M         3\n",
      "   ital_supr_s                  The Italian Supreme Pizza    S         1\n",
      " ital_veggie_l               The Italian Vegetables Pizza    L         1\n",
      " ital_veggie_m               The Italian Vegetables Pizza    M         1\n",
      " ital_veggie_s               The Italian Vegetables Pizza    S         1\n",
      "mediterraneo_l                    The Mediterranean Pizza    L         1\n",
      "mediterraneo_m                    The Mediterranean Pizza    M         1\n",
      "mediterraneo_s                    The Mediterranean Pizza    S         1\n",
      "    mexicana_l                         The Mexicana Pizza    L         2\n",
      "    mexicana_m                         The Mexicana Pizza    M         1\n",
      "    mexicana_s                         The Mexicana Pizza    S         1\n",
      "  napolitana_l                       The Napolitana Pizza    L         1\n",
      "  napolitana_m                       The Napolitana Pizza    M         1\n",
      "  napolitana_s                       The Napolitana Pizza    S         1\n",
      " pep_msh_pep_l The Pepperoni, Mushroom, and Peppers Pizza    L         1\n",
      " pep_msh_pep_m The Pepperoni, Mushroom, and Peppers Pizza    M         1\n",
      " pep_msh_pep_s The Pepperoni, Mushroom, and Peppers Pizza    S         1\n",
      "   pepperoni_l                        The Pepperoni Pizza    L         2\n",
      "   pepperoni_m                        The Pepperoni Pizza    M         2\n",
      "   pepperoni_s                        The Pepperoni Pizza    S         2\n",
      "peppr_salami_l                    The Pepper Salami Pizza    L         2\n",
      "peppr_salami_m                    The Pepper Salami Pizza    M         1\n",
      "peppr_salami_s                    The Pepper Salami Pizza    S         1\n",
      "  prsc_argla_l           The Prosciutto and Arugula Pizza    L         1\n",
      "  prsc_argla_m           The Prosciutto and Arugula Pizza    M         2\n",
      "  prsc_argla_s           The Prosciutto and Arugula Pizza    S         1\n",
      "    sicilian_l                         The Sicilian Pizza    L         1\n",
      "    sicilian_m                         The Sicilian Pizza    M         1\n",
      "    sicilian_s                         The Sicilian Pizza    S         1\n",
      " soppressata_l                      The Soppressata Pizza    L         1\n",
      " soppressata_m                      The Soppressata Pizza    M         1\n",
      " soppressata_s                      The Soppressata Pizza    S         1\n",
      "  southw_ckn_l                The Southwest Chicken Pizza    L         3\n",
      "  southw_ckn_m                The Southwest Chicken Pizza    M         2\n",
      "  southw_ckn_s                The Southwest Chicken Pizza    S         1\n",
      "  spicy_ital_l                    The Spicy Italian Pizza    L         3\n",
      "  spicy_ital_m                    The Spicy Italian Pizza    M         1\n",
      "  spicy_ital_s                    The Spicy Italian Pizza    S         1\n",
      "  spin_pesto_l                    The Spinach Pesto Pizza    L         1\n",
      "  spin_pesto_m                    The Spinach Pesto Pizza    M         1\n",
      "  spin_pesto_s                    The Spinach Pesto Pizza    S         1\n",
      " spinach_fet_l                 The Spinach and Feta Pizza    L         1\n",
      " spinach_fet_m                 The Spinach and Feta Pizza    M         1\n",
      " spinach_fet_s                 The Spinach and Feta Pizza    S         1\n",
      "spinach_supr_l                  The Spinach Supreme Pizza    L         1\n",
      "spinach_supr_m                  The Spinach Supreme Pizza    M         1\n",
      "spinach_supr_s                  The Spinach Supreme Pizza    S         1\n",
      "    thai_ckn_l                     The Thai Chicken Pizza    L         4\n",
      "    thai_ckn_m                     The Thai Chicken Pizza    M         1\n",
      "    thai_ckn_s                     The Thai Chicken Pizza    S         1\n",
      "   the_greek_l                            The Greek Pizza    L         1\n",
      "   the_greek_m                            The Greek Pizza    M         1\n",
      "   the_greek_s                            The Greek Pizza    S         1\n",
      "  the_greek_xl                            The Greek Pizza    L         2\n",
      "  veggie_veg_l          The Vegetables + Vegetables Pizza    L         1\n",
      "  veggie_veg_m          The Vegetables + Vegetables Pizza    M         1\n",
      "  veggie_veg_s          The Vegetables + Vegetables Pizza    S         1\n",
      "\n",
      "Pizza Quantity Forecast for Day 4:\n",
      "\n",
      "          Code                                 Pizza Name Size  Quantity\n",
      "     bbq_ckn_l                 The Barbecue Chicken Pizza    L         2\n",
      "     bbq_ckn_m                 The Barbecue Chicken Pizza    M         2\n",
      "     bbq_ckn_s                 The Barbecue Chicken Pizza    S         2\n",
      "    big_meat_s                         The Big Meat Pizza    S         5\n",
      "  brie_carre_s                       The Brie Carre Pizza    S         1\n",
      "   calabrese_l                        The Calabrese Pizza    L         1\n",
      "   calabrese_m                        The Calabrese Pizza    M         1\n",
      "    cali_ckn_l               The California Chicken Pizza    L         3\n",
      "    cali_ckn_m               The California Chicken Pizza    M         2\n",
      "    cali_ckn_s               The California Chicken Pizza    S         2\n",
      " ckn_alfredo_l                  The Chicken Alfredo Pizza    L         1\n",
      " ckn_alfredo_m                  The Chicken Alfredo Pizza    M         2\n",
      "   ckn_pesto_l                    The Chicken Pesto Pizza    L         1\n",
      "   ckn_pesto_m                    The Chicken Pesto Pizza    M         1\n",
      "   ckn_pesto_s                    The Chicken Pesto Pizza    S         1\n",
      " classic_dlx_l                   The Classic Deluxe Pizza    L         1\n",
      " classic_dlx_m                   The Classic Deluxe Pizza    M         4\n",
      " classic_dlx_s                   The Classic Deluxe Pizza    S         2\n",
      " five_cheese_l                      The Five Cheese Pizza    L         3\n",
      " four_cheese_l                      The Four Cheese Pizza    L         3\n",
      " four_cheese_m                      The Four Cheese Pizza    M         2\n",
      "green_garden_m                     The Green Garden Pizza    M         1\n",
      "green_garden_s                     The Green Garden Pizza    S         2\n",
      "    hawaiian_l                         The Hawaiian Pizza    L         3\n",
      "    hawaiian_m                         The Hawaiian Pizza    M         1\n",
      "    hawaiian_s                         The Hawaiian Pizza    S         2\n",
      " ital_cpcllo_l                The Italian Capocollo Pizza    L         1\n",
      " ital_cpcllo_m                The Italian Capocollo Pizza    M         1\n",
      " ital_cpcllo_s                The Italian Capocollo Pizza    S         1\n",
      "   ital_supr_l                  The Italian Supreme Pizza    L         2\n",
      "   ital_supr_m                  The Italian Supreme Pizza    M         3\n",
      "   ital_supr_s                  The Italian Supreme Pizza    S         1\n",
      " ital_veggie_m               The Italian Vegetables Pizza    M         1\n",
      " ital_veggie_s               The Italian Vegetables Pizza    S         1\n",
      "mediterraneo_l                    The Mediterranean Pizza    L         1\n",
      "mediterraneo_m                    The Mediterranean Pizza    M         1\n",
      "mediterraneo_s                    The Mediterranean Pizza    S         1\n",
      "    mexicana_l                         The Mexicana Pizza    L         2\n",
      "    mexicana_m                         The Mexicana Pizza    M         1\n",
      "    mexicana_s                         The Mexicana Pizza    S         1\n",
      "  napolitana_l                       The Napolitana Pizza    L         1\n",
      "  napolitana_m                       The Napolitana Pizza    M         1\n",
      "  napolitana_s                       The Napolitana Pizza    S         1\n",
      " pep_msh_pep_l The Pepperoni, Mushroom, and Peppers Pizza    L         1\n",
      " pep_msh_pep_m The Pepperoni, Mushroom, and Peppers Pizza    M         1\n",
      " pep_msh_pep_s The Pepperoni, Mushroom, and Peppers Pizza    S         1\n",
      "   pepperoni_l                        The Pepperoni Pizza    L         2\n",
      "   pepperoni_m                        The Pepperoni Pizza    M         2\n",
      "   pepperoni_s                        The Pepperoni Pizza    S         2\n",
      "peppr_salami_l                    The Pepper Salami Pizza    L         2\n",
      "peppr_salami_m                    The Pepper Salami Pizza    M         1\n",
      "peppr_salami_s                    The Pepper Salami Pizza    S         1\n",
      "  prsc_argla_l           The Prosciutto and Arugula Pizza    L         1\n",
      "  prsc_argla_m           The Prosciutto and Arugula Pizza    M         1\n",
      "  prsc_argla_s           The Prosciutto and Arugula Pizza    S         1\n",
      "    sicilian_l                         The Sicilian Pizza    L         1\n",
      "    sicilian_m                         The Sicilian Pizza    M         1\n",
      "    sicilian_s                         The Sicilian Pizza    S         1\n",
      " soppressata_l                      The Soppressata Pizza    L         1\n",
      " soppressata_m                      The Soppressata Pizza    M         1\n",
      " soppressata_s                      The Soppressata Pizza    S         1\n",
      "  southw_ckn_l                The Southwest Chicken Pizza    L         2\n",
      "  southw_ckn_m                The Southwest Chicken Pizza    M         2\n",
      "  southw_ckn_s                The Southwest Chicken Pizza    S         1\n",
      "  spicy_ital_l                    The Spicy Italian Pizza    L         3\n",
      "  spicy_ital_m                    The Spicy Italian Pizza    M         1\n",
      "  spicy_ital_s                    The Spicy Italian Pizza    S         1\n",
      "  spin_pesto_l                    The Spinach Pesto Pizza    L         1\n",
      "  spin_pesto_m                    The Spinach Pesto Pizza    M         1\n",
      "  spin_pesto_s                    The Spinach Pesto Pizza    S         1\n",
      " spinach_fet_l                 The Spinach and Feta Pizza    L         1\n",
      " spinach_fet_m                 The Spinach and Feta Pizza    M         1\n",
      " spinach_fet_s                 The Spinach and Feta Pizza    S         1\n",
      "spinach_supr_l                  The Spinach Supreme Pizza    L         1\n",
      "spinach_supr_m                  The Spinach Supreme Pizza    M         1\n",
      "spinach_supr_s                  The Spinach Supreme Pizza    S         1\n",
      "    thai_ckn_l                     The Thai Chicken Pizza    L         4\n",
      "    thai_ckn_m                     The Thai Chicken Pizza    M         1\n",
      "    thai_ckn_s                     The Thai Chicken Pizza    S         1\n",
      "   the_greek_l                            The Greek Pizza    L         1\n",
      "   the_greek_m                            The Greek Pizza    M         1\n",
      "   the_greek_s                            The Greek Pizza    S         1\n",
      "  the_greek_xl                            The Greek Pizza    L         1\n",
      "  veggie_veg_l          The Vegetables + Vegetables Pizza    L         1\n",
      "  veggie_veg_m          The Vegetables + Vegetables Pizza    M         1\n",
      "  veggie_veg_s          The Vegetables + Vegetables Pizza    S         1\n",
      "\n",
      "Pizza Quantity Forecast for Day 5:\n",
      "\n",
      "          Code                                 Pizza Name Size  Quantity\n",
      "     bbq_ckn_l                 The Barbecue Chicken Pizza    L         2\n",
      "     bbq_ckn_m                 The Barbecue Chicken Pizza    M         2\n",
      "     bbq_ckn_s                 The Barbecue Chicken Pizza    S         2\n",
      "    big_meat_s                         The Big Meat Pizza    S         5\n",
      "  brie_carre_s                       The Brie Carre Pizza    S         1\n",
      "   calabrese_l                        The Calabrese Pizza    L         1\n",
      "   calabrese_m                        The Calabrese Pizza    M         1\n",
      "    cali_ckn_l               The California Chicken Pizza    L         3\n",
      "    cali_ckn_m               The California Chicken Pizza    M         2\n",
      "    cali_ckn_s               The California Chicken Pizza    S         2\n",
      " ckn_alfredo_l                  The Chicken Alfredo Pizza    L         1\n",
      " ckn_alfredo_m                  The Chicken Alfredo Pizza    M         2\n",
      "   ckn_pesto_l                    The Chicken Pesto Pizza    L         1\n",
      "   ckn_pesto_m                    The Chicken Pesto Pizza    M         1\n",
      "   ckn_pesto_s                    The Chicken Pesto Pizza    S         1\n",
      " classic_dlx_l                   The Classic Deluxe Pizza    L         1\n",
      " classic_dlx_m                   The Classic Deluxe Pizza    M         4\n",
      " classic_dlx_s                   The Classic Deluxe Pizza    S         2\n",
      " five_cheese_l                      The Five Cheese Pizza    L         3\n",
      " four_cheese_l                      The Four Cheese Pizza    L         3\n",
      " four_cheese_m                      The Four Cheese Pizza    M         1\n",
      "green_garden_m                     The Green Garden Pizza    M         1\n",
      "green_garden_s                     The Green Garden Pizza    S         2\n",
      "    hawaiian_l                         The Hawaiian Pizza    L         3\n",
      "    hawaiian_m                         The Hawaiian Pizza    M         1\n",
      "    hawaiian_s                         The Hawaiian Pizza    S         2\n",
      " ital_cpcllo_l                The Italian Capocollo Pizza    L         1\n",
      " ital_cpcllo_m                The Italian Capocollo Pizza    M         1\n",
      " ital_cpcllo_s                The Italian Capocollo Pizza    S         1\n",
      "   ital_supr_l                  The Italian Supreme Pizza    L         2\n",
      "   ital_supr_m                  The Italian Supreme Pizza    M         3\n",
      "   ital_supr_s                  The Italian Supreme Pizza    S         1\n",
      " ital_veggie_m               The Italian Vegetables Pizza    M         1\n",
      " ital_veggie_s               The Italian Vegetables Pizza    S         1\n",
      "mediterraneo_l                    The Mediterranean Pizza    L         1\n",
      "mediterraneo_m                    The Mediterranean Pizza    M         1\n",
      "mediterraneo_s                    The Mediterranean Pizza    S         1\n",
      "    mexicana_l                         The Mexicana Pizza    L         2\n",
      "    mexicana_m                         The Mexicana Pizza    M         1\n",
      "    mexicana_s                         The Mexicana Pizza    S         1\n",
      "  napolitana_l                       The Napolitana Pizza    L         1\n",
      "  napolitana_m                       The Napolitana Pizza    M         1\n",
      "  napolitana_s                       The Napolitana Pizza    S         1\n",
      " pep_msh_pep_l The Pepperoni, Mushroom, and Peppers Pizza    L         1\n",
      " pep_msh_pep_m The Pepperoni, Mushroom, and Peppers Pizza    M         1\n",
      " pep_msh_pep_s The Pepperoni, Mushroom, and Peppers Pizza    S         1\n",
      "   pepperoni_l                        The Pepperoni Pizza    L         2\n",
      "   pepperoni_m                        The Pepperoni Pizza    M         2\n",
      "   pepperoni_s                        The Pepperoni Pizza    S         2\n",
      "peppr_salami_l                    The Pepper Salami Pizza    L         2\n",
      "peppr_salami_m                    The Pepper Salami Pizza    M         1\n",
      "peppr_salami_s                    The Pepper Salami Pizza    S         1\n",
      "  prsc_argla_l           The Prosciutto and Arugula Pizza    L         1\n",
      "  prsc_argla_m           The Prosciutto and Arugula Pizza    M         1\n",
      "  prsc_argla_s           The Prosciutto and Arugula Pizza    S         1\n",
      "    sicilian_l                         The Sicilian Pizza    L         1\n",
      "    sicilian_m                         The Sicilian Pizza    M         1\n",
      "    sicilian_s                         The Sicilian Pizza    S         1\n",
      " soppressata_l                      The Soppressata Pizza    L         1\n",
      " soppressata_m                      The Soppressata Pizza    M         1\n",
      " soppressata_s                      The Soppressata Pizza    S         1\n",
      "  southw_ckn_l                The Southwest Chicken Pizza    L         2\n",
      "  southw_ckn_m                The Southwest Chicken Pizza    M         2\n",
      "  southw_ckn_s                The Southwest Chicken Pizza    S         1\n",
      "  spicy_ital_l                    The Spicy Italian Pizza    L         3\n",
      "  spicy_ital_m                    The Spicy Italian Pizza    M         1\n",
      "  spicy_ital_s                    The Spicy Italian Pizza    S         1\n",
      "  spin_pesto_l                    The Spinach Pesto Pizza    L         1\n",
      "  spin_pesto_m                    The Spinach Pesto Pizza    M         1\n",
      "  spin_pesto_s                    The Spinach Pesto Pizza    S         1\n",
      " spinach_fet_l                 The Spinach and Feta Pizza    L         1\n",
      " spinach_fet_m                 The Spinach and Feta Pizza    M         1\n",
      " spinach_fet_s                 The Spinach and Feta Pizza    S         1\n",
      "spinach_supr_l                  The Spinach Supreme Pizza    L         1\n",
      "spinach_supr_m                  The Spinach Supreme Pizza    M         1\n",
      "spinach_supr_s                  The Spinach Supreme Pizza    S         1\n",
      "    thai_ckn_l                     The Thai Chicken Pizza    L         4\n",
      "    thai_ckn_m                     The Thai Chicken Pizza    M         1\n",
      "    thai_ckn_s                     The Thai Chicken Pizza    S         1\n",
      "   the_greek_l                            The Greek Pizza    L         1\n",
      "   the_greek_m                            The Greek Pizza    M         1\n",
      "   the_greek_s                            The Greek Pizza    S         1\n",
      "  the_greek_xl                            The Greek Pizza    L         1\n",
      "  veggie_veg_l          The Vegetables + Vegetables Pizza    L         1\n",
      "  veggie_veg_m          The Vegetables + Vegetables Pizza    M         1\n",
      "  veggie_veg_s          The Vegetables + Vegetables Pizza    S         1\n",
      "\n",
      "Pizza Quantity Forecast for Day 6:\n",
      "\n",
      "          Code                                 Pizza Name Size  Quantity\n",
      "     bbq_ckn_l                 The Barbecue Chicken Pizza    L         3\n",
      "     bbq_ckn_m                 The Barbecue Chicken Pizza    M         2\n",
      "     bbq_ckn_s                 The Barbecue Chicken Pizza    S         2\n",
      "    big_meat_s                         The Big Meat Pizza    S         5\n",
      "  brie_carre_s                       The Brie Carre Pizza    S         1\n",
      "   calabrese_l                        The Calabrese Pizza    L         1\n",
      "   calabrese_m                        The Calabrese Pizza    M         1\n",
      "    cali_ckn_l               The California Chicken Pizza    L         3\n",
      "    cali_ckn_m               The California Chicken Pizza    M         2\n",
      "    cali_ckn_s               The California Chicken Pizza    S         2\n",
      " ckn_alfredo_l                  The Chicken Alfredo Pizza    L         1\n",
      " ckn_alfredo_m                  The Chicken Alfredo Pizza    M         2\n",
      "   ckn_pesto_l                    The Chicken Pesto Pizza    L         1\n",
      "   ckn_pesto_m                    The Chicken Pesto Pizza    M         1\n",
      "   ckn_pesto_s                    The Chicken Pesto Pizza    S         1\n",
      " classic_dlx_l                   The Classic Deluxe Pizza    L         1\n",
      " classic_dlx_m                   The Classic Deluxe Pizza    M         4\n",
      " classic_dlx_s                   The Classic Deluxe Pizza    S         2\n",
      " five_cheese_l                      The Five Cheese Pizza    L         3\n",
      " four_cheese_l                      The Four Cheese Pizza    L         3\n",
      " four_cheese_m                      The Four Cheese Pizza    M         1\n",
      "green_garden_m                     The Green Garden Pizza    M         1\n",
      "green_garden_s                     The Green Garden Pizza    S         2\n",
      "    hawaiian_l                         The Hawaiian Pizza    L         2\n",
      "    hawaiian_m                         The Hawaiian Pizza    M         1\n",
      "    hawaiian_s                         The Hawaiian Pizza    S         2\n",
      " ital_cpcllo_l                The Italian Capocollo Pizza    L         1\n",
      " ital_cpcllo_m                The Italian Capocollo Pizza    M         1\n",
      " ital_cpcllo_s                The Italian Capocollo Pizza    S         1\n",
      "   ital_supr_l                  The Italian Supreme Pizza    L         2\n",
      "   ital_supr_m                  The Italian Supreme Pizza    M         3\n",
      "   ital_supr_s                  The Italian Supreme Pizza    S         1\n",
      " ital_veggie_m               The Italian Vegetables Pizza    M         1\n",
      " ital_veggie_s               The Italian Vegetables Pizza    S         1\n",
      "mediterraneo_l                    The Mediterranean Pizza    L         1\n",
      "mediterraneo_m                    The Mediterranean Pizza    M         1\n",
      "mediterraneo_s                    The Mediterranean Pizza    S         1\n",
      "    mexicana_l                         The Mexicana Pizza    L         2\n",
      "    mexicana_m                         The Mexicana Pizza    M         1\n",
      "    mexicana_s                         The Mexicana Pizza    S         1\n",
      "  napolitana_l                       The Napolitana Pizza    L         1\n",
      "  napolitana_m                       The Napolitana Pizza    M         1\n",
      "  napolitana_s                       The Napolitana Pizza    S         1\n",
      " pep_msh_pep_l The Pepperoni, Mushroom, and Peppers Pizza    L         1\n",
      " pep_msh_pep_m The Pepperoni, Mushroom, and Peppers Pizza    M         1\n",
      " pep_msh_pep_s The Pepperoni, Mushroom, and Peppers Pizza    S         1\n",
      "   pepperoni_l                        The Pepperoni Pizza    L         2\n",
      "   pepperoni_m                        The Pepperoni Pizza    M         2\n",
      "   pepperoni_s                        The Pepperoni Pizza    S         2\n",
      "peppr_salami_l                    The Pepper Salami Pizza    L         2\n",
      "peppr_salami_m                    The Pepper Salami Pizza    M         1\n",
      "peppr_salami_s                    The Pepper Salami Pizza    S         1\n",
      "  prsc_argla_l           The Prosciutto and Arugula Pizza    L         1\n",
      "  prsc_argla_m           The Prosciutto and Arugula Pizza    M         1\n",
      "  prsc_argla_s           The Prosciutto and Arugula Pizza    S         1\n",
      "    sicilian_l                         The Sicilian Pizza    L         1\n",
      "    sicilian_m                         The Sicilian Pizza    M         1\n",
      "    sicilian_s                         The Sicilian Pizza    S         1\n",
      " soppressata_l                      The Soppressata Pizza    L         1\n",
      " soppressata_m                      The Soppressata Pizza    M         1\n",
      "  southw_ckn_l                The Southwest Chicken Pizza    L         2\n",
      "  southw_ckn_m                The Southwest Chicken Pizza    M         2\n",
      "  southw_ckn_s                The Southwest Chicken Pizza    S         1\n",
      "  spicy_ital_l                    The Spicy Italian Pizza    L         3\n",
      "  spicy_ital_m                    The Spicy Italian Pizza    M         1\n",
      "  spicy_ital_s                    The Spicy Italian Pizza    S         1\n",
      "  spin_pesto_l                    The Spinach Pesto Pizza    L         1\n",
      "  spin_pesto_m                    The Spinach Pesto Pizza    M         1\n",
      "  spin_pesto_s                    The Spinach Pesto Pizza    S         1\n",
      " spinach_fet_l                 The Spinach and Feta Pizza    L         1\n",
      " spinach_fet_m                 The Spinach and Feta Pizza    M         1\n",
      " spinach_fet_s                 The Spinach and Feta Pizza    S         1\n",
      "spinach_supr_l                  The Spinach Supreme Pizza    L         1\n",
      "spinach_supr_m                  The Spinach Supreme Pizza    M         1\n",
      "spinach_supr_s                  The Spinach Supreme Pizza    S         1\n",
      "    thai_ckn_l                     The Thai Chicken Pizza    L         4\n",
      "    thai_ckn_m                     The Thai Chicken Pizza    M         1\n",
      "    thai_ckn_s                     The Thai Chicken Pizza    S         1\n",
      "   the_greek_l                            The Greek Pizza    L         1\n",
      "   the_greek_m                            The Greek Pizza    M         1\n",
      "   the_greek_s                            The Greek Pizza    S         1\n",
      "  the_greek_xl                            The Greek Pizza    L         1\n",
      "  veggie_veg_l          The Vegetables + Vegetables Pizza    L         1\n",
      "  veggie_veg_m          The Vegetables + Vegetables Pizza    M         1\n",
      "  veggie_veg_s          The Vegetables + Vegetables Pizza    S         1\n",
      "\n",
      "Pizza Quantity Forecast for Day 7:\n",
      "\n",
      "          Code                                 Pizza Name Size  Quantity\n",
      "     bbq_ckn_l                 The Barbecue Chicken Pizza    L         3\n",
      "     bbq_ckn_m                 The Barbecue Chicken Pizza    M         2\n",
      "     bbq_ckn_s                 The Barbecue Chicken Pizza    S         1\n",
      "    big_meat_s                         The Big Meat Pizza    S         5\n",
      "  brie_carre_s                       The Brie Carre Pizza    S         1\n",
      "   calabrese_l                        The Calabrese Pizza    L         1\n",
      "   calabrese_m                        The Calabrese Pizza    M         1\n",
      "    cali_ckn_l               The California Chicken Pizza    L         3\n",
      "    cali_ckn_m               The California Chicken Pizza    M         2\n",
      "    cali_ckn_s               The California Chicken Pizza    S         2\n",
      " ckn_alfredo_l                  The Chicken Alfredo Pizza    L         1\n",
      " ckn_alfredo_m                  The Chicken Alfredo Pizza    M         2\n",
      "   ckn_pesto_l                    The Chicken Pesto Pizza    L         1\n",
      "   ckn_pesto_m                    The Chicken Pesto Pizza    M         1\n",
      "   ckn_pesto_s                    The Chicken Pesto Pizza    S         1\n",
      " classic_dlx_l                   The Classic Deluxe Pizza    L         1\n",
      " classic_dlx_m                   The Classic Deluxe Pizza    M         3\n",
      " classic_dlx_s                   The Classic Deluxe Pizza    S         2\n",
      " five_cheese_l                      The Five Cheese Pizza    L         3\n",
      " four_cheese_l                      The Four Cheese Pizza    L         3\n",
      " four_cheese_m                      The Four Cheese Pizza    M         1\n",
      "green_garden_m                     The Green Garden Pizza    M         1\n",
      "green_garden_s                     The Green Garden Pizza    S         2\n",
      "    hawaiian_l                         The Hawaiian Pizza    L         2\n",
      "    hawaiian_m                         The Hawaiian Pizza    M         1\n",
      "    hawaiian_s                         The Hawaiian Pizza    S         2\n",
      " ital_cpcllo_l                The Italian Capocollo Pizza    L         1\n",
      " ital_cpcllo_m                The Italian Capocollo Pizza    M         1\n",
      " ital_cpcllo_s                The Italian Capocollo Pizza    S         1\n",
      "   ital_supr_l                  The Italian Supreme Pizza    L         2\n",
      "   ital_supr_m                  The Italian Supreme Pizza    M         3\n",
      "   ital_supr_s                  The Italian Supreme Pizza    S         1\n",
      " ital_veggie_m               The Italian Vegetables Pizza    M         1\n",
      " ital_veggie_s               The Italian Vegetables Pizza    S         1\n",
      "mediterraneo_l                    The Mediterranean Pizza    L         1\n",
      "mediterraneo_m                    The Mediterranean Pizza    M         1\n",
      "mediterraneo_s                    The Mediterranean Pizza    S         1\n",
      "    mexicana_l                         The Mexicana Pizza    L         2\n",
      "    mexicana_m                         The Mexicana Pizza    M         1\n",
      "    mexicana_s                         The Mexicana Pizza    S         1\n",
      "  napolitana_l                       The Napolitana Pizza    L         1\n",
      "  napolitana_m                       The Napolitana Pizza    M         1\n",
      "  napolitana_s                       The Napolitana Pizza    S         1\n",
      " pep_msh_pep_l The Pepperoni, Mushroom, and Peppers Pizza    L         1\n",
      " pep_msh_pep_m The Pepperoni, Mushroom, and Peppers Pizza    M         1\n",
      " pep_msh_pep_s The Pepperoni, Mushroom, and Peppers Pizza    S         1\n",
      "   pepperoni_l                        The Pepperoni Pizza    L         2\n",
      "   pepperoni_m                        The Pepperoni Pizza    M         2\n",
      "   pepperoni_s                        The Pepperoni Pizza    S         2\n",
      "peppr_salami_l                    The Pepper Salami Pizza    L         2\n",
      "peppr_salami_m                    The Pepper Salami Pizza    M         1\n",
      "peppr_salami_s                    The Pepper Salami Pizza    S         1\n",
      "  prsc_argla_l           The Prosciutto and Arugula Pizza    L         1\n",
      "  prsc_argla_m           The Prosciutto and Arugula Pizza    M         1\n",
      "  prsc_argla_s           The Prosciutto and Arugula Pizza    S         1\n",
      "    sicilian_l                         The Sicilian Pizza    L         1\n",
      "    sicilian_m                         The Sicilian Pizza    M         1\n",
      "    sicilian_s                         The Sicilian Pizza    S         1\n",
      " soppressata_l                      The Soppressata Pizza    L         1\n",
      " soppressata_m                      The Soppressata Pizza    M         1\n",
      "  southw_ckn_l                The Southwest Chicken Pizza    L         2\n",
      "  southw_ckn_m                The Southwest Chicken Pizza    M         2\n",
      "  southw_ckn_s                The Southwest Chicken Pizza    S         1\n",
      "  spicy_ital_l                    The Spicy Italian Pizza    L         3\n",
      "  spicy_ital_m                    The Spicy Italian Pizza    M         1\n",
      "  spicy_ital_s                    The Spicy Italian Pizza    S         1\n",
      "  spin_pesto_l                    The Spinach Pesto Pizza    L         1\n",
      "  spin_pesto_m                    The Spinach Pesto Pizza    M         1\n",
      "  spin_pesto_s                    The Spinach Pesto Pizza    S         1\n",
      " spinach_fet_l                 The Spinach and Feta Pizza    L         1\n",
      " spinach_fet_m                 The Spinach and Feta Pizza    M         1\n",
      " spinach_fet_s                 The Spinach and Feta Pizza    S         1\n",
      "spinach_supr_l                  The Spinach Supreme Pizza    L         1\n",
      "spinach_supr_m                  The Spinach Supreme Pizza    M         1\n",
      "spinach_supr_s                  The Spinach Supreme Pizza    S         1\n",
      "    thai_ckn_l                     The Thai Chicken Pizza    L         4\n",
      "    thai_ckn_m                     The Thai Chicken Pizza    M         1\n",
      "    thai_ckn_s                     The Thai Chicken Pizza    S         1\n",
      "   the_greek_l                            The Greek Pizza    L         1\n",
      "   the_greek_m                            The Greek Pizza    M         1\n",
      "   the_greek_s                            The Greek Pizza    S         1\n",
      "  the_greek_xl                            The Greek Pizza    L         1\n",
      "  veggie_veg_l          The Vegetables + Vegetables Pizza    L         1\n",
      "  veggie_veg_m          The Vegetables + Vegetables Pizza    M         1\n",
      "  veggie_veg_s          The Vegetables + Vegetables Pizza    S         1\n"
     ]
    }
   ],
   "source": [
    "# Creating Order List for each day\n",
    "for day, quantities in enumerate(rounded_predictions):\n",
    "    order_list = []\n",
    "    \n",
    "    for i, quantity in enumerate(quantities):\n",
    "        if quantity > 0:\n",
    "            pizza_code = pizza_names[i]\n",
    "            \n",
    "            # Debug: Print pizza_code to check if it matches df_ing pizza_name_id\n",
    "            #print(f\"Pizza code: {pizza_code}\")\n",
    "            \n",
    "            # Get pizza name and size from df_ing\n",
    "            pizza_info = df_ing[df_ing['pizza_name_id'] == pizza_code]\n",
    "            \n",
    "            if pizza_info.empty:\n",
    "                print(f\"No match found for {pizza_code} in df_ing\")\n",
    "            else:\n",
    "                pizza_name = pizza_info['pizza_name'].values[0]\n",
    "                pizza_size = pizza_info['pizza_size'].values[0]\n",
    "                \n",
    "                # Add to order list\n",
    "                order_list.append({\n",
    "                    'Code': pizza_code,\n",
    "                    'Pizza Name': pizza_name,\n",
    "                    'Size': pizza_size,\n",
    "                    'Quantity': quantity\n",
    "                })\n",
    "    \n",
    "    # Convert the list of orders to a DataFrame for this day\n",
    "    df_order = pd.DataFrame(order_list)\n",
    "    \n",
    "    # Display the order for the day as a table\n",
    "    print(f\"\\nPizza Quantity Forecast for Day {day + 1}:\\n\")\n",
    "    print(df_order.to_string(index=False))\n"
   ]
  },
  {
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   "execution_count": 41,
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      "text/plain": [
       "    bbq_ckn_l  bbq_ckn_m  bbq_ckn_s  big_meat_s  brie_carre_s  calabrese_l  \\\n",
       "0           3          3          1           4             1            0   \n",
       "1           3          2          1           5             1            0   \n",
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       "64          2          2          2           5             1            1   \n",
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       "\n",
       "    calabrese_m  calabrese_s  cali_ckn_l  cali_ckn_m  ...  thai_ckn_m  \\\n",
       "0             1            0           3           2  ...           1   \n",
       "1             1            0           2           2  ...           1   \n",
       "2             1            0           2           1  ...           1   \n",
       "3             1            0           2           2  ...           1   \n",
       "4             2            0           2           3  ...           1   \n",
       "..          ...          ...         ...         ...  ...         ...   \n",
       "61            1            0           3           2  ...           2   \n",
       "62            1            0           3           2  ...           2   \n",
       "63            1            0           2           2  ...           1   \n",
       "64            1            0           2           2  ...           1   \n",
       "65            1            0           2           2  ...           1   \n",
       "\n",
       "    thai_ckn_s  the_greek_l  the_greek_m  the_greek_s  the_greek_xl  \\\n",
       "0            2            1            1            1             2   \n",
       "1            2            1            1            1             1   \n",
       "2            2            1            1            1             1   \n",
       "3            2            1            1            1             1   \n",
       "4            2            1            1            1             1   \n",
       "..         ...          ...          ...          ...           ...   \n",
       "61           1            1            1            1             1   \n",
       "62           2            1            1            1             1   \n",
       "63           2            1            1            1             2   \n",
       "64           2            1            1            1             1   \n",
       "65           2            1            1            1             1   \n",
       "\n",
       "    the_greek_xxl  veggie_veg_l  veggie_veg_m  veggie_veg_s  \n",
       "0               0             1             2             2  \n",
       "1               0             1             1             1  \n",
       "2               0             1             2             1  \n",
       "3               0             1             2             2  \n",
       "4               0             1             2             2  \n",
       "..            ...           ...           ...           ...  \n",
       "61              0             1             2             1  \n",
       "62              0             1             1             2  \n",
       "63              0             1             1             2  \n",
       "64              0             1             1             2  \n",
       "65              0             1             1             1  \n",
       "\n",
       "[66 rows x 91 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
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   ],
   "source": [
    "pizza_quantities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "f9836857-a770-46f4-981d-2fc96c5194f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File saved as: pizza_order_forecast_Jan01_to_Jan07.xlsx\n"
     ]
    }
   ],
   "source": [
    "# Saving to excel for easy viewing \n",
    "\n",
    "file_name_pizza_order = f\"pizza_order_forecast_{next_1_day.strftime('%b%d')}_to_{next_7_days.strftime('%b%d')}.xlsx\"\n",
    "\n",
    "# Create a Pandas Excel writer object\n",
    "with pd.ExcelWriter(file_name_pizza_order, engine='openpyxl') as writer:\n",
    "    # Loop through each day's pizza quantities\n",
    "    for day, quantities in enumerate(rounded_predictions):\n",
    "        order_list = []\n",
    "        \n",
    "        for i, quantity in enumerate(quantities):\n",
    "            if quantity > 0:\n",
    "                pizza_code = pizza_names[i]\n",
    "                \n",
    "                # Get pizza name and size from df_ing\n",
    "                pizza_info = df_ing[df_ing['pizza_name_id'] == pizza_code]\n",
    "                \n",
    "                if not pizza_info.empty:\n",
    "                    pizza_name = pizza_info['pizza_name'].values[0]\n",
    "                    pizza_size = pizza_info['pizza_size'].values[0]\n",
    "                    \n",
    "                    # Add to order list\n",
    "                    order_list.append({\n",
    "                        'Code': pizza_code,\n",
    "                        'Pizza Name': pizza_name,\n",
    "                        'Size': pizza_size,\n",
    "                        'Quantity': quantity\n",
    "                    })\n",
    "        \n",
    "        # Convert the list of orders to a DataFrame for this day\n",
    "        df_order = pd.DataFrame(order_list)\n",
    "        \n",
    "        # Write the DataFrame to a sheet in the Excel file\n",
    "        df_order.to_excel(writer, sheet_name=f'Day_{day+1}', index=False)\n",
    "\n",
    "print(f\"File saved as: {file_name_pizza_order}\")"
   ]
  },
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   "execution_count": 86,
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 92 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   bbq_ckn_l  bbq_ckn_m  bbq_ckn_s  big_meat_s  brie_carre_s  calabrese_l  \\\n",
       "0          3          2          2           6             1            2   \n",
       "1          2          2          2           5             1            1   \n",
       "2          2          2          2           5             1            1   \n",
       "3          2          2          2           5             1            1   \n",
       "4          2          2          2           5             1            1   \n",
       "5          3          2          2           5             1            1   \n",
       "6          3          2          1           5             1            1   \n",
       "\n",
       "   calabrese_m  calabrese_s  cali_ckn_l  cali_ckn_m  ...  thai_ckn_s  \\\n",
       "0            1            0           2           3  ...           2   \n",
       "1            1            0           2           2  ...           1   \n",
       "2            1            0           3           2  ...           1   \n",
       "3            1            0           3           2  ...           1   \n",
       "4            1            0           3           2  ...           1   \n",
       "5            1            0           3           2  ...           1   \n",
       "6            1            0           3           2  ...           1   \n",
       "\n",
       "   the_greek_l  the_greek_m  the_greek_s  the_greek_xl  the_greek_xxl  \\\n",
       "0            1            1            1             2              0   \n",
       "1            0            1            1             1              0   \n",
       "2            1            1            1             2              0   \n",
       "3            1            1            1             1              0   \n",
       "4            1            1            1             1              0   \n",
       "5            1            1            1             1              0   \n",
       "6            1            1            1             1              0   \n",
       "\n",
       "   veggie_veg_l  veggie_veg_m  veggie_veg_s  day  \n",
       "0             1             2             1    1  \n",
       "1             1             1             1    2  \n",
       "2             1             1             1    3  \n",
       "3             1             1             1    4  \n",
       "4             1             1             1    5  \n",
       "5             1             1             1    6  \n",
       "6             1             1             1    7  \n",
       "\n",
       "[7 rows x 92 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating quantity amount in grams of each ingredient per day\n",
    "# Determine the number of rows in the DataFrame\n",
    "num_rows = len(df_forecast)\n",
    "\n",
    "# Add a day column with values from 1 to num_rows\n",
    "df_forecast['day'] = [(i % num_rows) + 1 for i in range(num_rows)]\n",
    "\n",
    "df_forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "472207a0-a583-46d6-bb6a-b08bade49d7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>pizza_name_id</th>\n",
       "      <th>forecast_qty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>bbq_ckn_l</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>bbq_ckn_l</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>bbq_ckn_l</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>bbq_ckn_l</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>bbq_ckn_l</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>3</td>\n",
       "      <td>veggie_veg_s</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>4</td>\n",
       "      <td>veggie_veg_s</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>5</td>\n",
       "      <td>veggie_veg_s</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>6</td>\n",
       "      <td>veggie_veg_s</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>7</td>\n",
       "      <td>veggie_veg_s</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>637 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     day pizza_name_id  forecast_qty\n",
       "0      1     bbq_ckn_l             3\n",
       "1      2     bbq_ckn_l             2\n",
       "2      3     bbq_ckn_l             2\n",
       "3      4     bbq_ckn_l             2\n",
       "4      5     bbq_ckn_l             2\n",
       "..   ...           ...           ...\n",
       "632    3  veggie_veg_s             1\n",
       "633    4  veggie_veg_s             1\n",
       "634    5  veggie_veg_s             1\n",
       "635    6  veggie_veg_s             1\n",
       "636    7  veggie_veg_s             1\n",
       "\n",
       "[637 rows x 3 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reshape df_forecast to long format\n",
    "df_forecast_long = df_forecast.melt(id_vars=['day'], var_name='pizza_name_id', value_name='forecast_qty')\n",
    "df_forecast_long"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "3e7231c7-38f6-43cd-98a5-ba1f247a1516",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .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>day</th>\n",
       "      <th>pizza_name_id</th>\n",
       "      <th>forecast_qty</th>\n",
       "      <th>pizza_name</th>\n",
       "      <th>pizza_ingredients</th>\n",
       "      <th>Items_Qty_In_Grams</th>\n",
       "      <th>pizza_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>bbq_ckn_l</td>\n",
       "      <td>3</td>\n",
       "      <td>The Barbecue Chicken Pizza</td>\n",
       "      <td>Barbecued Chicken</td>\n",
       "      <td>40</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>bbq_ckn_l</td>\n",
       "      <td>3</td>\n",
       "      <td>The Barbecue Chicken Pizza</td>\n",
       "      <td>Red Peppers</td>\n",
       "      <td>15</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>bbq_ckn_l</td>\n",
       "      <td>3</td>\n",
       "      <td>The Barbecue Chicken Pizza</td>\n",
       "      <td>Green Peppers</td>\n",
       "      <td>20</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>bbq_ckn_l</td>\n",
       "      <td>3</td>\n",
       "      <td>The Barbecue Chicken Pizza</td>\n",
       "      <td>Tomatoes</td>\n",
       "      <td>30</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>bbq_ckn_l</td>\n",
       "      <td>3</td>\n",
       "      <td>The Barbecue Chicken Pizza</td>\n",
       "      <td>Red Onions</td>\n",
       "      <td>60</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3621</th>\n",
       "      <td>7</td>\n",
       "      <td>veggie_veg_s</td>\n",
       "      <td>1</td>\n",
       "      <td>The Vegetables + Vegetables Pizza</td>\n",
       "      <td>Green Peppers</td>\n",
       "      <td>20</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3622</th>\n",
       "      <td>7</td>\n",
       "      <td>veggie_veg_s</td>\n",
       "      <td>1</td>\n",
       "      <td>The Vegetables + Vegetables Pizza</td>\n",
       "      <td>Red Onions</td>\n",
       "      <td>20</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3623</th>\n",
       "      <td>7</td>\n",
       "      <td>veggie_veg_s</td>\n",
       "      <td>1</td>\n",
       "      <td>The Vegetables + Vegetables Pizza</td>\n",
       "      <td>Zucchini</td>\n",
       "      <td>10</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3624</th>\n",
       "      <td>7</td>\n",
       "      <td>veggie_veg_s</td>\n",
       "      <td>1</td>\n",
       "      <td>The Vegetables + Vegetables Pizza</td>\n",
       "      <td>Spinach</td>\n",
       "      <td>15</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3625</th>\n",
       "      <td>7</td>\n",
       "      <td>veggie_veg_s</td>\n",
       "      <td>1</td>\n",
       "      <td>The Vegetables + Vegetables Pizza</td>\n",
       "      <td>Garlic</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3626 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      day pizza_name_id  forecast_qty                         pizza_name  \\\n",
       "0       1     bbq_ckn_l             3         The Barbecue Chicken Pizza   \n",
       "1       1     bbq_ckn_l             3         The Barbecue Chicken Pizza   \n",
       "2       1     bbq_ckn_l             3         The Barbecue Chicken Pizza   \n",
       "3       1     bbq_ckn_l             3         The Barbecue Chicken Pizza   \n",
       "4       1     bbq_ckn_l             3         The Barbecue Chicken Pizza   \n",
       "...   ...           ...           ...                                ...   \n",
       "3621    7  veggie_veg_s             1  The Vegetables + Vegetables Pizza   \n",
       "3622    7  veggie_veg_s             1  The Vegetables + Vegetables Pizza   \n",
       "3623    7  veggie_veg_s             1  The Vegetables + Vegetables Pizza   \n",
       "3624    7  veggie_veg_s             1  The Vegetables + Vegetables Pizza   \n",
       "3625    7  veggie_veg_s             1  The Vegetables + Vegetables Pizza   \n",
       "\n",
       "      pizza_ingredients  Items_Qty_In_Grams pizza_size  \n",
       "0     Barbecued Chicken                  40          L  \n",
       "1           Red Peppers                  15          L  \n",
       "2         Green Peppers                  20          L  \n",
       "3              Tomatoes                  30          L  \n",
       "4            Red Onions                  60          L  \n",
       "...                 ...                 ...        ...  \n",
       "3621      Green Peppers                  20          S  \n",
       "3622         Red Onions                  20          S  \n",
       "3623           Zucchini                  10          S  \n",
       "3624            Spinach                  15          S  \n",
       "3625             Garlic                   5          S  \n",
       "\n",
       "[3626 rows x 7 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge forecast data with ingredient quantities\n",
    "df_merged = pd.merge(df_forecast_long, df_ing, on='pizza_name_id')\n",
    "df_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "6bda6e75-6352-4cde-9375-b15bbef76b43",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>pizza_name_id</th>\n",
       "      <th>forecast_qty</th>\n",
       "      <th>pizza_name</th>\n",
       "      <th>pizza_ingredients</th>\n",
       "      <th>Items_Qty_In_Grams</th>\n",
       "      <th>pizza_size</th>\n",
       "      <th>total_qty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>bbq_ckn_l</td>\n",
       "      <td>3</td>\n",
       "      <td>The Barbecue Chicken Pizza</td>\n",
       "      <td>Barbecued Chicken</td>\n",
       "      <td>40</td>\n",
       "      <td>L</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>bbq_ckn_l</td>\n",
       "      <td>3</td>\n",
       "      <td>The Barbecue Chicken Pizza</td>\n",
       "      <td>Red Peppers</td>\n",
       "      <td>15</td>\n",
       "      <td>L</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>bbq_ckn_l</td>\n",
       "      <td>3</td>\n",
       "      <td>The Barbecue Chicken Pizza</td>\n",
       "      <td>Green Peppers</td>\n",
       "      <td>20</td>\n",
       "      <td>L</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>bbq_ckn_l</td>\n",
       "      <td>3</td>\n",
       "      <td>The Barbecue Chicken Pizza</td>\n",
       "      <td>Tomatoes</td>\n",
       "      <td>30</td>\n",
       "      <td>L</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>bbq_ckn_l</td>\n",
       "      <td>3</td>\n",
       "      <td>The Barbecue Chicken Pizza</td>\n",
       "      <td>Red Onions</td>\n",
       "      <td>60</td>\n",
       "      <td>L</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3621</th>\n",
       "      <td>7</td>\n",
       "      <td>veggie_veg_s</td>\n",
       "      <td>1</td>\n",
       "      <td>The Vegetables + Vegetables Pizza</td>\n",
       "      <td>Green Peppers</td>\n",
       "      <td>20</td>\n",
       "      <td>S</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3622</th>\n",
       "      <td>7</td>\n",
       "      <td>veggie_veg_s</td>\n",
       "      <td>1</td>\n",
       "      <td>The Vegetables + Vegetables Pizza</td>\n",
       "      <td>Red Onions</td>\n",
       "      <td>20</td>\n",
       "      <td>S</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3623</th>\n",
       "      <td>7</td>\n",
       "      <td>veggie_veg_s</td>\n",
       "      <td>1</td>\n",
       "      <td>The Vegetables + Vegetables Pizza</td>\n",
       "      <td>Zucchini</td>\n",
       "      <td>10</td>\n",
       "      <td>S</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3624</th>\n",
       "      <td>7</td>\n",
       "      <td>veggie_veg_s</td>\n",
       "      <td>1</td>\n",
       "      <td>The Vegetables + Vegetables Pizza</td>\n",
       "      <td>Spinach</td>\n",
       "      <td>15</td>\n",
       "      <td>S</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3625</th>\n",
       "      <td>7</td>\n",
       "      <td>veggie_veg_s</td>\n",
       "      <td>1</td>\n",
       "      <td>The Vegetables + Vegetables Pizza</td>\n",
       "      <td>Garlic</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3626 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      day pizza_name_id  forecast_qty                         pizza_name  \\\n",
       "0       1     bbq_ckn_l             3         The Barbecue Chicken Pizza   \n",
       "1       1     bbq_ckn_l             3         The Barbecue Chicken Pizza   \n",
       "2       1     bbq_ckn_l             3         The Barbecue Chicken Pizza   \n",
       "3       1     bbq_ckn_l             3         The Barbecue Chicken Pizza   \n",
       "4       1     bbq_ckn_l             3         The Barbecue Chicken Pizza   \n",
       "...   ...           ...           ...                                ...   \n",
       "3621    7  veggie_veg_s             1  The Vegetables + Vegetables Pizza   \n",
       "3622    7  veggie_veg_s             1  The Vegetables + Vegetables Pizza   \n",
       "3623    7  veggie_veg_s             1  The Vegetables + Vegetables Pizza   \n",
       "3624    7  veggie_veg_s             1  The Vegetables + Vegetables Pizza   \n",
       "3625    7  veggie_veg_s             1  The Vegetables + Vegetables Pizza   \n",
       "\n",
       "      pizza_ingredients  Items_Qty_In_Grams pizza_size  total_qty  \n",
       "0     Barbecued Chicken                  40          L        120  \n",
       "1           Red Peppers                  15          L         45  \n",
       "2         Green Peppers                  20          L         60  \n",
       "3              Tomatoes                  30          L         90  \n",
       "4            Red Onions                  60          L        180  \n",
       "...                 ...                 ...        ...        ...  \n",
       "3621      Green Peppers                  20          S         20  \n",
       "3622         Red Onions                  20          S         20  \n",
       "3623           Zucchini                  10          S         10  \n",
       "3624            Spinach                  15          S         15  \n",
       "3625             Garlic                   5          S          5  \n",
       "\n",
       "[3626 rows x 8 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate total quantity of each ingredient required per day\n",
    "df_merged['total_qty'] = df_merged['forecast_qty'] * df_merged['Items_Qty_In_Grams']\n",
    "df_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "9dff05b2-bb56-42cf-bbf9-95453c5c38f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Day 1\n",
      "         pizza_ingredients  total_qty\n",
      "              ?duja Salami        160\n",
      "             Alfredo Sauce         30\n",
      "                 Anchovies        130\n",
      "                Artichokes        360\n",
      "                   Arugula         40\n",
      "             Asiago Cheese        190\n",
      "                     Bacon       1520\n",
      "            Barbecue Sauce         75\n",
      "         Barbecued Chicken        220\n",
      "          Beef Chuck Roast        520\n",
      "               Blue Cheese        120\n",
      "         Brie Carre Cheese         30\n",
      "          Calabrese Salami        700\n",
      "                 Capocollo       2400\n",
      "        Caramelized Onions         15\n",
      "                   Chicken       2700\n",
      "            Chipotle Sauce        260\n",
      "           Chorizo Sausage        300\n",
      "                  Cilantro        130\n",
      "    Coarse Sicilian Salami        360\n",
      "                      Corn        870\n",
      "                  Eggplant         60\n",
      "               Feta Cheese        410\n",
      "            Fontina Cheese        190\n",
      "       Friggitello Peppers         40\n",
      "                    Garlic        915\n",
      "              Genoa Salami        270\n",
      "               Goat Cheese        500\n",
      "Gorgonzola Piccante Cheese        160\n",
      "              Gouda Cheese        130\n",
      "              Green Olives        195\n",
      "             Green Peppers        290\n",
      "           Italian Sausage         60\n",
      "          Jalapeno Peppers        195\n",
      "           Kalamata Olives        125\n",
      "          Luganega Sausage        180\n",
      "         Mozzarella Cheese        630\n",
      "                 Mushrooms       1080\n",
      "                    Onions         90\n",
      "                   Oregano         45\n",
      "                  Pancetta        240\n",
      "Parmigiano Reggiano Cheese        390\n",
      "                     Pears         10\n",
      "         Peperoncini verdi         80\n",
      "                 Pepperoni       1280\n",
      "               Pesto Sauce        150\n",
      "                 Pineapple        540\n",
      "             Plum Tomatoes        100\n",
      "                Prosciutto         30\n",
      " Prosciutto di San Daniele         90\n",
      "          Provolone Cheese        120\n",
      "                Red Onions       2580\n",
      "               Red Peppers        515\n",
      "            Ricotta Cheese        200\n",
      "             Romano Cheese        120\n",
      "                Sliced Ham        255\n",
      "       Smoked Gouda Cheese        120\n",
      "        Soppressata Salami        300\n",
      "                   Spinach        870\n",
      "        Sun-dried Tomatoes         55\n",
      "   Thai Sweet Chilli Sauce        190\n",
      "                     Thyme          5\n",
      "                  Tomatoes       1840\n",
      "                  Zucchini        110\n",
      "\n",
      "Day 2\n",
      "         pizza_ingredients  total_qty\n",
      "              ?duja Salami        100\n",
      "             Alfredo Sauce         30\n",
      "                 Anchovies         85\n",
      "                Artichokes        335\n",
      "                   Arugula         30\n",
      "             Asiago Cheese        190\n",
      "                     Bacon       1270\n",
      "            Barbecue Sauce         60\n",
      "         Barbecued Chicken        180\n",
      "          Beef Chuck Roast        270\n",
      "               Blue Cheese         90\n",
      "         Brie Carre Cheese         30\n",
      "          Calabrese Salami        650\n",
      "                 Capocollo       2150\n",
      "        Caramelized Onions         15\n",
      "                   Chicken       2400\n",
      "            Chipotle Sauce        210\n",
      "           Chorizo Sausage        250\n",
      "                  Cilantro        105\n",
      "    Coarse Sicilian Salami        280\n",
      "                      Corn        700\n",
      "                  Eggplant        120\n",
      "               Feta Cheese        260\n",
      "            Fontina Cheese        170\n",
      "       Friggitello Peppers         25\n",
      "                    Garlic        765\n",
      "              Genoa Salami        270\n",
      "               Goat Cheese        420\n",
      "Gorgonzola Piccante Cheese        160\n",
      "              Gouda Cheese        110\n",
      "              Green Olives        150\n",
      "             Green Peppers        220\n",
      "           Italian Sausage         50\n",
      "          Jalapeno Peppers        160\n",
      "           Kalamata Olives         90\n",
      "          Luganega Sausage        140\n",
      "         Mozzarella Cheese        550\n",
      "                 Mushrooms        860\n",
      "                    Onions         70\n",
      "                   Oregano         45\n",
      "                  Pancetta        150\n",
      "Parmigiano Reggiano Cheese        390\n",
      "                     Pears         10\n",
      "         Peperoncini verdi         60\n",
      "                 Pepperoni       1130\n",
      "               Pesto Sauce        180\n",
      "                 Pineapple        435\n",
      "             Plum Tomatoes        100\n",
      "                Prosciutto         30\n",
      " Prosciutto di San Daniele         70\n",
      "          Provolone Cheese         90\n",
      "                Red Onions       2040\n",
      "               Red Peppers        450\n",
      "            Ricotta Cheese        200\n",
      "             Romano Cheese         90\n",
      "                Sliced Ham        210\n",
      "       Smoked Gouda Cheese         90\n",
      "        Soppressata Salami        300\n",
      "                   Spinach        750\n",
      "        Sun-dried Tomatoes         55\n",
      "   Thai Sweet Chilli Sauce        150\n",
      "                     Thyme          5\n",
      "                  Tomatoes       1500\n",
      "                  Zucchini        120\n",
      "\n",
      "Day 3\n",
      "         pizza_ingredients  total_qty\n",
      "              ?duja Salami        100\n",
      "             Alfredo Sauce         30\n",
      "                 Anchovies         85\n",
      "                Artichokes        375\n",
      "                   Arugula         40\n",
      "             Asiago Cheese        190\n",
      "                     Bacon       1370\n",
      "            Barbecue Sauce         60\n",
      "         Barbecued Chicken        180\n",
      "          Beef Chuck Roast        520\n",
      "               Blue Cheese        120\n",
      "         Brie Carre Cheese         30\n",
      "          Calabrese Salami        650\n",
      "                 Capocollo       2150\n",
      "        Caramelized Onions         15\n",
      "                   Chicken       2700\n",
      "            Chipotle Sauce        230\n",
      "           Chorizo Sausage        250\n",
      "                  Cilantro        115\n",
      "    Coarse Sicilian Salami        240\n",
      "                      Corn        770\n",
      "                  Eggplant        120\n",
      "               Feta Cheese        370\n",
      "            Fontina Cheese        210\n",
      "       Friggitello Peppers         25\n",
      "                    Garlic        830\n",
      "              Genoa Salami        270\n",
      "               Goat Cheese        420\n",
      "Gorgonzola Piccante Cheese        160\n",
      "              Gouda Cheese        150\n",
      "              Green Olives        145\n",
      "             Green Peppers        220\n",
      "           Italian Sausage         50\n",
      "          Jalapeno Peppers        190\n",
      "           Kalamata Olives        130\n",
      "          Luganega Sausage        120\n",
      "         Mozzarella Cheese        590\n",
      "                 Mushrooms        900\n",
      "                    Onions         60\n",
      "                   Oregano         45\n",
      "                  Pancetta        150\n",
      "Parmigiano Reggiano Cheese        390\n",
      "                     Pears         10\n",
      "         Peperoncini verdi         60\n",
      "                 Pepperoni       1170\n",
      "               Pesto Sauce        180\n",
      "                 Pineapple        420\n",
      "             Plum Tomatoes        120\n",
      "                Prosciutto         30\n",
      " Prosciutto di San Daniele         90\n",
      "          Provolone Cheese        120\n",
      "                Red Onions       2280\n",
      "               Red Peppers        470\n",
      "            Ricotta Cheese        200\n",
      "             Romano Cheese        120\n",
      "                Sliced Ham        195\n",
      "       Smoked Gouda Cheese        120\n",
      "        Soppressata Salami        300\n",
      "                   Spinach        825\n",
      "        Sun-dried Tomatoes         60\n",
      "   Thai Sweet Chilli Sauce        150\n",
      "                     Thyme          5\n",
      "                  Tomatoes       1580\n",
      "                  Zucchini        120\n",
      "\n",
      "Day 4\n",
      "         pizza_ingredients  total_qty\n",
      "              ?duja Salami        100\n",
      "             Alfredo Sauce         50\n",
      "                 Anchovies         85\n",
      "                Artichokes        355\n",
      "                   Arugula         30\n",
      "             Asiago Cheese        220\n",
      "                     Bacon       1370\n",
      "            Barbecue Sauce         60\n",
      "         Barbecued Chicken        180\n",
      "          Beef Chuck Roast        370\n",
      "               Blue Cheese         90\n",
      "         Brie Carre Cheese         30\n",
      "          Calabrese Salami        650\n",
      "                 Capocollo       2000\n",
      "        Caramelized Onions         15\n",
      "                   Chicken       2700\n",
      "            Chipotle Sauce        200\n",
      "           Chorizo Sausage        250\n",
      "                  Cilantro        100\n",
      "    Coarse Sicilian Salami        240\n",
      "                      Corn        670\n",
      "                  Eggplant         60\n",
      "               Feta Cheese        310\n",
      "            Fontina Cheese        210\n",
      "       Friggitello Peppers         25\n",
      "                    Garlic        765\n",
      "              Genoa Salami        270\n",
      "               Goat Cheese        360\n",
      "Gorgonzola Piccante Cheese        160\n",
      "              Gouda Cheese        150\n",
      "              Green Olives        145\n",
      "             Green Peppers        220\n",
      "           Italian Sausage         50\n",
      "          Jalapeno Peppers        175\n",
      "           Kalamata Olives        110\n",
      "          Luganega Sausage        120\n",
      "         Mozzarella Cheese        540\n",
      "                 Mushrooms        960\n",
      "                    Onions         60\n",
      "                   Oregano         30\n",
      "                  Pancetta        150\n",
      "Parmigiano Reggiano Cheese        390\n",
      "                     Pears         10\n",
      "         Peperoncini verdi         60\n",
      "                 Pepperoni       1170\n",
      "               Pesto Sauce        150\n",
      "                 Pineapple        420\n",
      "             Plum Tomatoes        120\n",
      "                Prosciutto         30\n",
      " Prosciutto di San Daniele         70\n",
      "          Provolone Cheese         90\n",
      "                Red Onions       2200\n",
      "               Red Peppers        440\n",
      "            Ricotta Cheese        200\n",
      "             Romano Cheese         90\n",
      "                Sliced Ham        195\n",
      "       Smoked Gouda Cheese         90\n",
      "        Soppressata Salami        300\n",
      "                   Spinach        825\n",
      "        Sun-dried Tomatoes         60\n",
      "   Thai Sweet Chilli Sauce        150\n",
      "                     Thyme          5\n",
      "                  Tomatoes       1450\n",
      "                  Zucchini         90\n",
      "\n",
      "Day 5\n",
      "         pizza_ingredients  total_qty\n",
      "              ?duja Salami        100\n",
      "             Alfredo Sauce         50\n",
      "                 Anchovies         85\n",
      "                Artichokes        355\n",
      "                   Arugula         30\n",
      "             Asiago Cheese        220\n",
      "                     Bacon       1370\n",
      "            Barbecue Sauce         60\n",
      "         Barbecued Chicken        180\n",
      "          Beef Chuck Roast        370\n",
      "               Blue Cheese         90\n",
      "         Brie Carre Cheese         30\n",
      "          Calabrese Salami        650\n",
      "                 Capocollo       2000\n",
      "        Caramelized Onions         15\n",
      "                   Chicken       2700\n",
      "            Chipotle Sauce        200\n",
      "           Chorizo Sausage        250\n",
      "                  Cilantro        100\n",
      "    Coarse Sicilian Salami        240\n",
      "                      Corn        670\n",
      "                  Eggplant         60\n",
      "               Feta Cheese        310\n",
      "            Fontina Cheese        210\n",
      "       Friggitello Peppers         25\n",
      "                    Garlic        755\n",
      "              Genoa Salami        270\n",
      "               Goat Cheese        360\n",
      "Gorgonzola Piccante Cheese        140\n",
      "              Gouda Cheese        150\n",
      "              Green Olives        145\n",
      "             Green Peppers        220\n",
      "           Italian Sausage         50\n",
      "          Jalapeno Peppers        175\n",
      "           Kalamata Olives        110\n",
      "          Luganega Sausage        120\n",
      "         Mozzarella Cheese        530\n",
      "                 Mushrooms        960\n",
      "                    Onions         60\n",
      "                   Oregano         30\n",
      "                  Pancetta        150\n",
      "Parmigiano Reggiano Cheese        330\n",
      "                     Pears         10\n",
      "         Peperoncini verdi         60\n",
      "                 Pepperoni       1170\n",
      "               Pesto Sauce        150\n",
      "                 Pineapple        420\n",
      "             Plum Tomatoes        120\n",
      "                Prosciutto         30\n",
      " Prosciutto di San Daniele         70\n",
      "          Provolone Cheese         90\n",
      "                Red Onions       2200\n",
      "               Red Peppers        440\n",
      "            Ricotta Cheese        175\n",
      "             Romano Cheese         90\n",
      "                Sliced Ham        195\n",
      "       Smoked Gouda Cheese         90\n",
      "        Soppressata Salami        300\n",
      "                   Spinach        825\n",
      "        Sun-dried Tomatoes         60\n",
      "   Thai Sweet Chilli Sauce        150\n",
      "                     Thyme          5\n",
      "                  Tomatoes       1450\n",
      "                  Zucchini         90\n",
      "\n",
      "Day 6\n",
      "         pizza_ingredients  total_qty\n",
      "              ?duja Salami        100\n",
      "             Alfredo Sauce         50\n",
      "                 Anchovies         85\n",
      "                Artichokes        355\n",
      "                   Arugula         30\n",
      "             Asiago Cheese        220\n",
      "                     Bacon       1370\n",
      "            Barbecue Sauce         75\n",
      "         Barbecued Chicken        220\n",
      "          Beef Chuck Roast        370\n",
      "               Blue Cheese         90\n",
      "         Brie Carre Cheese         30\n",
      "          Calabrese Salami        650\n",
      "                 Capocollo       2000\n",
      "        Caramelized Onions         15\n",
      "                   Chicken       2700\n",
      "            Chipotle Sauce        200\n",
      "           Chorizo Sausage        250\n",
      "                  Cilantro        100\n",
      "    Coarse Sicilian Salami        240\n",
      "                      Corn        670\n",
      "                  Eggplant         60\n",
      "               Feta Cheese        310\n",
      "            Fontina Cheese        200\n",
      "       Friggitello Peppers         25\n",
      "                    Garlic        750\n",
      "              Genoa Salami        270\n",
      "               Goat Cheese        360\n",
      "Gorgonzola Piccante Cheese        140\n",
      "              Gouda Cheese        150\n",
      "              Green Olives        145\n",
      "             Green Peppers        240\n",
      "           Italian Sausage         50\n",
      "          Jalapeno Peppers        175\n",
      "           Kalamata Olives        110\n",
      "          Luganega Sausage        120\n",
      "         Mozzarella Cheese        490\n",
      "                 Mushrooms        940\n",
      "                    Onions         60\n",
      "                   Oregano         30\n",
      "                  Pancetta        150\n",
      "Parmigiano Reggiano Cheese        330\n",
      "                     Pears         10\n",
      "         Peperoncini verdi         60\n",
      "                 Pepperoni       1170\n",
      "               Pesto Sauce        150\n",
      "                 Pineapple        375\n",
      "             Plum Tomatoes        120\n",
      "                Prosciutto         30\n",
      " Prosciutto di San Daniele         70\n",
      "          Provolone Cheese         90\n",
      "                Red Onions       2260\n",
      "               Red Peppers        455\n",
      "            Ricotta Cheese        175\n",
      "             Romano Cheese         90\n",
      "                Sliced Ham        150\n",
      "       Smoked Gouda Cheese         90\n",
      "        Soppressata Salami        250\n",
      "                   Spinach        825\n",
      "        Sun-dried Tomatoes         60\n",
      "   Thai Sweet Chilli Sauce        150\n",
      "                     Thyme          5\n",
      "                  Tomatoes       1480\n",
      "                  Zucchini         90\n",
      "\n",
      "Day 7\n",
      "         pizza_ingredients  total_qty\n",
      "              ?duja Salami        100\n",
      "             Alfredo Sauce         50\n",
      "                 Anchovies         85\n",
      "                Artichokes        355\n",
      "                   Arugula         30\n",
      "             Asiago Cheese        220\n",
      "                     Bacon       1270\n",
      "            Barbecue Sauce         70\n",
      "         Barbecued Chicken        200\n",
      "          Beef Chuck Roast        370\n",
      "               Blue Cheese         90\n",
      "         Brie Carre Cheese         30\n",
      "          Calabrese Salami        650\n",
      "                 Capocollo       2000\n",
      "        Caramelized Onions         15\n",
      "                   Chicken       2700\n",
      "            Chipotle Sauce        200\n",
      "           Chorizo Sausage        250\n",
      "                  Cilantro        100\n",
      "    Coarse Sicilian Salami        240\n",
      "                      Corn        670\n",
      "                  Eggplant         60\n",
      "               Feta Cheese        310\n",
      "            Fontina Cheese        200\n",
      "       Friggitello Peppers         25\n",
      "                    Garlic        750\n",
      "              Genoa Salami        270\n",
      "               Goat Cheese        360\n",
      "Gorgonzola Piccante Cheese        140\n",
      "              Gouda Cheese        150\n",
      "              Green Olives        145\n",
      "             Green Peppers        220\n",
      "           Italian Sausage         50\n",
      "          Jalapeno Peppers        175\n",
      "           Kalamata Olives        110\n",
      "          Luganega Sausage        120\n",
      "         Mozzarella Cheese        490\n",
      "                 Mushrooms        900\n",
      "                    Onions         60\n",
      "                   Oregano         30\n",
      "                  Pancetta        150\n",
      "Parmigiano Reggiano Cheese        330\n",
      "                     Pears         10\n",
      "         Peperoncini verdi         60\n",
      "                 Pepperoni       1130\n",
      "               Pesto Sauce        150\n",
      "                 Pineapple        375\n",
      "             Plum Tomatoes        120\n",
      "                Prosciutto         30\n",
      " Prosciutto di San Daniele         70\n",
      "          Provolone Cheese         90\n",
      "                Red Onions       2200\n",
      "               Red Peppers        440\n",
      "            Ricotta Cheese        175\n",
      "             Romano Cheese         90\n",
      "                Sliced Ham        150\n",
      "       Smoked Gouda Cheese         90\n",
      "        Soppressata Salami        250\n",
      "                   Spinach        825\n",
      "        Sun-dried Tomatoes         60\n",
      "   Thai Sweet Chilli Sauce        150\n",
      "                     Thyme          5\n",
      "                  Tomatoes       1470\n",
      "                  Zucchini         90\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Aggregate total quantities by day and pizza ingredient\n",
    "df_total_qty_by_day_ingredient = df_merged.groupby(['day', 'pizza_ingredients'])['total_qty'].sum().reset_index()\n",
    "\n",
    "# Print results in the desired format\n",
    "for day in df_total_qty_by_day_ingredient['day'].unique():\n",
    "    print(f\"Day {day}\")\n",
    "    df_day = df_total_qty_by_day_ingredient[df_total_qty_by_day_ingredient['day'] == day]\n",
    "    print(df_day[['pizza_ingredients', 'total_qty']].to_string(index=False))\n",
    "    print()  # Newline for better readability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "55a21954-b87b-4b9f-aa39-613976532b0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File saved as: pizza_quantities_Jan01_to_Jan07.xlsx\n"
     ]
    }
   ],
   "source": [
    "#Create excel file for easy viewing \n",
    "\n",
    "file_name_pizza_quantities = f\"pizza_quantities_{next_1_day.strftime('%b%d')}_to_{next_7_days.strftime('%b%d')}.xlsx\"\n",
    "\n",
    "with pd.ExcelWriter(file_name_pizza_quantities, engine='openpyxl') as writer:\n",
    "    # Iterate through each unique day\n",
    "    for day in df_total_qty_by_day_ingredient['day'].unique():\n",
    "        # Filter data for the current day\n",
    "        df_day = df_total_qty_by_day_ingredient[df_total_qty_by_day_ingredient['day'] == day]\n",
    "        \n",
    "        # Write the DataFrame to a new sheet in the Excel file\n",
    "        df_day[['pizza_ingredients', 'total_qty']].to_excel(writer, sheet_name=f'Day_{day}', index=False)\n",
    "\n",
    "print(f\"File saved as: {file_name_pizza_quantities}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abaa2455-2e39-496a-a0b3-345933456135",
   "metadata": {},
   "source": [
    "# Conclusion Summary\n",
    "- In this project, a comprehensive approach was taken to enhance Domino's pizza order forecasting capabilities:\n",
    "\n",
    "### Data Cleaning and Preparation:\n",
    "\n",
    "- The data was meticulously cleaned and preprocessed to ensure accuracy and consistency. This involved handling missing values, standardizing data formats, and consolidating relevant information.\n",
    "\n",
    "### Model Development:\n",
    "\n",
    "- A Long Short-Term Memory (LSTM) network was developed for predicting the quantities of 91 unique pizza orders and their associated ingredients over the next 7 days. This advanced machine learning model was chosen for its effectiveness in time series forecasting.\n",
    "\n",
    "### Model Evaluation:\n",
    "\n",
    "- The LSTM model was evaluated using Mean Absolute Error (MAE), achieving a score of 0.98. This reflects the model's high accuracy and reliability in making forecasts.\n",
    "\n",
    "### Results and Output:\n",
    "\n",
    "- Forecasts for the next 7 days were generated, detailing predictions for each pizza order and ingredient quantities in grams. These predictions were then saved to Excel files for convenient review and offline access.\n",
    "\n",
    "### Applications and Benefits:\n",
    "\n",
    "- Accurate forecasting of pizza orders and ingredient quantities provides significant benefits. It facilitates improved inventory management, optimized supply chain logistics, and enhanced operational efficiency for Domino's. By anticipating demand more accurately, Domino's can reduce waste, ensure timely ingredient availability, and boost overall customer satisfaction.\n",
    "\n",
    "In summary, this project showcases a successful application of predictive analytics in the pizza industry, demonstrating the ability to drive actionable insights and operational improvements through advanced machine learning techniques."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b73947c-59b0-49f8-a076-8e930356031f",
   "metadata": {},
   "outputs": [],
   "source": []
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