Food Demand Forecasting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a0cdf0bf-3162-4cf6-a223-d2f9587bd77f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "train = pd.read_csv('~/Downloads/Train.csv')\n",
    "test = pd.read_csv('~/Downloads/Test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "56246377-9db2-49f7-bf58-6e9dd0a0e5de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(321437, 13)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "98149aa5-963f-464f-81e8-40e1a6f76406",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets combine the Dataset\n",
    "\n",
    "combined = pd.concat([train, test], ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5a1e9fb3-1f52-452d-a167-c9db3b6b55d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [10,9])\n",
    "num_cols = ['Billing_Amount', 'Labelled_Price', 'Orders_Count']\n",
    "\n",
    "nrows = 2\n",
    "ncols = 2\n",
    "iterator = 1\n",
    "\n",
    "for i in num_cols:\n",
    "  plt.subplot(nrows, ncols, iterator)\n",
    "  sns.distplot(combined.loc[:, i])\n",
    "  iterator += 1\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dcc16598-59b2-4831-aed3-5c0e25729b20",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "07db4a62-343b-4eed-9830-7063e97637b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period_No                                0\n",
       "Facility_No                              0\n",
       "Facility_Category                        0\n",
       "City_Zip_Code                            0\n",
       "Operational_Region_Coverage_Area         0\n",
       "Billing_Amount                           0\n",
       "Labelled_Price                           0\n",
       "Custom_Promoted                          0\n",
       "Promoted                                 0\n",
       "Search_Promotions                        0\n",
       "Orders_Count                        118070\n",
       "Course                                   0\n",
       "Flavour_Profile                          0\n",
       "Quantity                                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6b382cfd-5fc9-465c-8114-4744cb28b1c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "pvalues = []\n",
    "for i in num_cols:\n",
    "    teststats, pvalue = stats.ttest_ind(train.loc[:, i], train.Orders_Count)\n",
    "    pvalues.append(pvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6c273cc0-1828-42bb-b0f3-77470af5c34e",
   "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>Pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Billing_Amount</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Labelled_Price</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orders_Count</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Pvalue\n",
       "Billing_Amount     0.0\n",
       "Labelled_Price     0.0\n",
       "Orders_Count       1.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(pvalues, index = num_cols, columns = [\"Pvalue\"])\n",
    "# All features are Important                                                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4d630f4d-9182-431c-a1bf-215a14823ef1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         df        sum_sq       mean_sq             F  \\\n",
      "Facility_Category       2.0  3.853415e+08  1.926708e+08   1521.332329   \n",
      "Period_No               1.0  4.469243e+07  4.469243e+07    352.892363   \n",
      "Custom_Promoted         1.0  4.108911e+09  4.108911e+09  32444.050565   \n",
      "Promoted                1.0  1.903102e+09  1.903102e+09  15026.930995   \n",
      "Search_Promotions       1.0  1.947709e+08  1.947709e+08   1537.915236   \n",
      "Residual           321430.0  4.070785e+10  1.266461e+05           NaN   \n",
      "\n",
      "                         PR(>F)  \n",
      "Facility_Category  0.000000e+00  \n",
      "Period_No          1.095383e-78  \n",
      "Custom_Promoted    0.000000e+00  \n",
      "Promoted           0.000000e+00  \n",
      "Search_Promotions  0.000000e+00  \n",
      "Residual                    NaN  \n"
     ]
    }
   ],
   "source": [
    "cat_cols=['Period_No', 'Custom_Promoted', 'Promoted', 'Search_Promotions', 'Facility_Category']\n",
    "\n",
    "import statsmodels.formula.api as sfa\n",
    "\n",
    "from statsmodels.stats.anova import anova_lm\n",
    "\n",
    "model = sfa.ols(\"Orders_Count~Period_No+Custom_Promoted+Promoted+Search_Promotions+Facility_Category\",\n",
    "        data = combined).fit()\n",
    "\n",
    "print(anova_lm(model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5bbdf12c",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"Quantity\"]= np.round(combined[\"Labelled_Price\"]/combined[\"Billing_Amount\"])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bbf8caef-41f7-4e60-8237-183ff627a9d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Period_No</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>1</th>\n",
       "      <td>3250.0</td>\n",
       "      <td>205.896000</td>\n",
       "      <td>253.210473</td>\n",
       "      <td>13.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>2822.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3290.0</td>\n",
       "      <td>242.832219</td>\n",
       "      <td>289.594577</td>\n",
       "      <td>13.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>2715.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3286.0</td>\n",
       "      <td>229.356665</td>\n",
       "      <td>277.997060</td>\n",
       "      <td>13.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>3172.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3212.0</td>\n",
       "      <td>228.412827</td>\n",
       "      <td>277.741486</td>\n",
       "      <td>13.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>2848.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3240.0</td>\n",
       "      <td>237.643210</td>\n",
       "      <td>294.750044</td>\n",
       "      <td>13.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>323.0</td>\n",
       "      <td>3012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>3097.0</td>\n",
       "      <td>286.625767</td>\n",
       "      <td>372.542244</td>\n",
       "      <td>13.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>378.0</td>\n",
       "      <td>5576.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>3109.0</td>\n",
       "      <td>299.257639</td>\n",
       "      <td>377.704739</td>\n",
       "      <td>13.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>6576.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>3107.0</td>\n",
       "      <td>416.708722</td>\n",
       "      <td>768.388798</td>\n",
       "      <td>13.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>9532.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>3041.0</td>\n",
       "      <td>264.857941</td>\n",
       "      <td>335.030524</td>\n",
       "      <td>13.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>3753.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>3043.0</td>\n",
       "      <td>280.674006</td>\n",
       "      <td>399.364206</td>\n",
       "      <td>13.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>9303.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            count        mean         std   min   25%    50%    75%     max\n",
       "Period_No                                                                  \n",
       "1          3250.0  205.896000  253.210473  13.0  53.0  122.0  269.0  2822.0\n",
       "2          3290.0  242.832219  289.594577  13.0  55.0  148.0  312.0  2715.0\n",
       "3          3286.0  229.356665  277.997060  13.0  54.0  135.0  296.0  3172.0\n",
       "4          3212.0  228.412827  277.741486  13.0  53.0  134.0  310.0  2848.0\n",
       "5          3240.0  237.643210  294.750044  13.0  53.0  123.0  323.0  3012.0\n",
       "...           ...         ...         ...   ...   ...    ...    ...     ...\n",
       "96         3097.0  286.625767  372.542244  13.0  55.0  162.0  378.0  5576.0\n",
       "97         3109.0  299.257639  377.704739  13.0  55.0  163.0  393.0  6576.0\n",
       "98         3107.0  416.708722  768.388798  13.0  67.0  161.0  405.0  9532.0\n",
       "99         3041.0  264.857941  335.030524  13.0  53.0  148.0  352.0  3753.0\n",
       "100        3043.0  280.674006  399.364206  13.0  54.0  149.0  352.0  9303.0\n",
       "\n",
       "[100 rows x 8 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Period No has entries till 100 resonating wth Order Count\n",
    "combined.groupby(\"Period_No\")[\"Orders_Count\"].describe()[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "100e7697-b6c3-47f3-99ff-9d82a2981d13",
   "metadata": {},
   "outputs": [],
   "source": [
    "#@combined.groupby(\"Facility_No\")[\"Orders_Count\"].describe()\n",
    "\n",
    "# Order Count Facility Wise\n",
    "\n",
    "combined[\"OC_FW\"] = combined.groupby(\"Facility_No\")[\"Orders_Count\"].transform('count')\n",
    "combined[\"AOC_FW\"] = combined.groupby(\"Facility_No\")[\"Orders_Count\"].transform('mean')\n",
    "combined[\"MOC_FW\"] = combined.groupby(\"Facility_No\")[\"Orders_Count\"].transform('median')\n",
    "combined[\"MinOC_FW\"] = combined.groupby(\"Facility_No\")[\"Orders_Count\"].transform('min')\n",
    "combined[\"MaxAOC_FW\"] = combined.groupby(\"Facility_No\")[\"Orders_Count\"].transform('max')\n",
    "combined[\"SDAOC_FW\"] = combined.groupby(\"Facility_No\")[\"Orders_Count\"].transform('std')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "795ad19e-f2f8-4bf7-92b4-0026f0073ac3",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"Magic1\"] = combined.groupby(\"City_Zip_Code\")[\"Orders_Count\"].transform('count')\n",
    "combined[\"Magic2\"] = combined.groupby(\"City_Zip_Code\")[\"Orders_Count\"].transform('mean')\n",
    "combined[\"Magic3\"] = combined.groupby(\"City_Zip_Code\")[\"Orders_Count\"].transform('median')\n",
    "combined[\"Magic4\"] = combined.groupby(\"City_Zip_Code\")[\"Orders_Count\"].transform('min')\n",
    "combined[\"Magic5\"] = combined.groupby(\"City_Zip_Code\")[\"Orders_Count\"].transform('max')\n",
    "combined[\"Magic6\"] = combined.groupby(\"City_Zip_Code\")[\"Orders_Count\"].transform('std')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "113c8830-eab1-4e9d-af1d-39069ec0f10c",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined.drop([\"Facility_No\", \"City_Zip_Code\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4ad629a8-cc75-41d3-99fb-f57670371374",
   "metadata": {},
   "outputs": [],
   "source": [
    "#combined.groupby(\"Operational_Region_Coverage_Area\")[\"Orders_Count\"].describe()\n",
    "\n",
    "combined[\"Magic7\"] = combined.groupby(\"Operational_Region_Coverage_Area\")[\"Orders_Count\"].transform('count')\n",
    "combined[\"Magic8\"] = combined.groupby(\"Operational_Region_Coverage_Area\")[\"Orders_Count\"].transform('mean')\n",
    "combined[\"Magic9\"] = combined.groupby(\"Operational_Region_Coverage_Area\")[\"Orders_Count\"].transform('median')\n",
    "combined[\"Magic10\"] = combined.groupby(\"Operational_Region_Coverage_Area\")[\"Orders_Count\"].transform('min')\n",
    "combined[\"Magic11\"] = combined.groupby(\"Operational_Region_Coverage_Area\")[\"Orders_Count\"].transform('max')\n",
    "combined[\"Magic12\"] = combined.groupby(\"Operational_Region_Coverage_Area\")[\"Orders_Count\"].transform('std')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "df193ecb-df6c-4c61-916a-5fd9c320afb1",
   "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>Period_No</th>\n",
       "      <th>Facility_Category</th>\n",
       "      <th>Operational_Region_Coverage_Area</th>\n",
       "      <th>Billing_Amount</th>\n",
       "      <th>Labelled_Price</th>\n",
       "      <th>Custom_Promoted</th>\n",
       "      <th>Promoted</th>\n",
       "      <th>Search_Promotions</th>\n",
       "      <th>Orders_Count</th>\n",
       "      <th>Course</th>\n",
       "      <th>Flavour_Profile</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>OC_FW</th>\n",
       "      <th>AOC_FW</th>\n",
       "      <th>MOC_FW</th>\n",
       "      <th>MinOC_FW</th>\n",
       "      <th>MaxAOC_FW</th>\n",
       "      <th>SDAOC_FW</th>\n",
       "      <th>Magic1</th>\n",
       "      <th>Magic2</th>\n",
       "      <th>Magic3</th>\n",
       "      <th>Magic4</th>\n",
       "      <th>Magic5</th>\n",
       "      <th>Magic6</th>\n",
       "      <th>Magic7</th>\n",
       "      <th>Magic8</th>\n",
       "      <th>Magic9</th>\n",
       "      <th>Magic10</th>\n",
       "      <th>Magic11</th>\n",
       "      <th>Magic12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>c1</td>\n",
       "      <td>169</td>\n",
       "      <td>192.955562</td>\n",
       "      <td>267.971749</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>Finger Foods</td>\n",
       "      <td>Asian Fusion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4118</td>\n",
       "      <td>207.096163</td>\n",
       "      <td>134.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3374.0</td>\n",
       "      <td>258.871277</td>\n",
       "      <td>66360</td>\n",
       "      <td>215.997107</td>\n",
       "      <td>134.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9532.0</td>\n",
       "      <td>289.687066</td>\n",
       "      <td>18103</td>\n",
       "      <td>313.646523</td>\n",
       "      <td>148.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10544.0</td>\n",
       "      <td>473.337118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>c3</td>\n",
       "      <td>62</td>\n",
       "      <td>193.384864</td>\n",
       "      <td>203.836706</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Broths &amp; Stews</td>\n",
       "      <td>Asian Fusion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4298</td>\n",
       "      <td>257.586087</td>\n",
       "      <td>109.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7088.0</td>\n",
       "      <td>430.839680</td>\n",
       "      <td>134451</td>\n",
       "      <td>318.114131</td>\n",
       "      <td>161.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13150.0</td>\n",
       "      <td>464.342597</td>\n",
       "      <td>36987</td>\n",
       "      <td>222.396247</td>\n",
       "      <td>122.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9532.0</td>\n",
       "      <td>329.431315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>c3</td>\n",
       "      <td>167</td>\n",
       "      <td>384.988183</td>\n",
       "      <td>388.957411</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>Smoothies &amp; Juices</td>\n",
       "      <td>Western</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3635</td>\n",
       "      <td>160.554058</td>\n",
       "      <td>95.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2930.0</td>\n",
       "      <td>216.484447</td>\n",
       "      <td>134451</td>\n",
       "      <td>318.114131</td>\n",
       "      <td>161.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13150.0</td>\n",
       "      <td>464.342597</td>\n",
       "      <td>6653</td>\n",
       "      <td>160.297760</td>\n",
       "      <td>94.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4009.0</td>\n",
       "      <td>224.186851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>c3</td>\n",
       "      <td>2</td>\n",
       "      <td>255.251864</td>\n",
       "      <td>263.923874</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>Grain Bowls</td>\n",
       "      <td>South Asian</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3261</td>\n",
       "      <td>200.477461</td>\n",
       "      <td>108.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4726.0</td>\n",
       "      <td>316.956115</td>\n",
       "      <td>81986</td>\n",
       "      <td>203.351487</td>\n",
       "      <td>122.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6426.0</td>\n",
       "      <td>263.878412</td>\n",
       "      <td>7575</td>\n",
       "      <td>244.712739</td>\n",
       "      <td>149.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4726.0</td>\n",
       "      <td>298.571900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>c3</td>\n",
       "      <td>18</td>\n",
       "      <td>319.949514</td>\n",
       "      <td>326.128813</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Appetizers</td>\n",
       "      <td>Asian Fusion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3240</td>\n",
       "      <td>168.157716</td>\n",
       "      <td>95.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2741.0</td>\n",
       "      <td>233.959012</td>\n",
       "      <td>81986</td>\n",
       "      <td>203.351487</td>\n",
       "      <td>122.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6426.0</td>\n",
       "      <td>263.878412</td>\n",
       "      <td>17767</td>\n",
       "      <td>184.067372</td>\n",
       "      <td>107.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5334.0</td>\n",
       "      <td>252.308016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Period_No Facility_Category  Operational_Region_Coverage_Area  \\\n",
       "0          1                c1                               169   \n",
       "1          1                c3                                62   \n",
       "2          1                c3                               167   \n",
       "3          1                c3                                 2   \n",
       "4          1                c3                                18   \n",
       "\n",
       "   Billing_Amount  Labelled_Price  Custom_Promoted  Promoted  \\\n",
       "0      192.955562      267.971749                0         0   \n",
       "1      193.384864      203.836706                0         0   \n",
       "2      384.988183      388.957411                0         0   \n",
       "3      255.251864      263.923874                0         0   \n",
       "4      319.949514      326.128813                0         0   \n",
       "\n",
       "   Search_Promotions  Orders_Count              Course Flavour_Profile  \\\n",
       "0                  0         123.0        Finger Foods    Asian Fusion   \n",
       "1                  0          55.0      Broths & Stews    Asian Fusion   \n",
       "2                  0         204.0  Smoothies & Juices         Western   \n",
       "3                  0         257.0         Grain Bowls     South Asian   \n",
       "4                  0          14.0          Appetizers    Asian Fusion   \n",
       "\n",
       "   Quantity  OC_FW      AOC_FW  MOC_FW  MinOC_FW  MaxAOC_FW    SDAOC_FW  \\\n",
       "0       1.0   4118  207.096163   134.0      13.0     3374.0  258.871277   \n",
       "1       1.0   4298  257.586087   109.0      13.0     7088.0  430.839680   \n",
       "2       1.0   3635  160.554058    95.0      13.0     2930.0  216.484447   \n",
       "3       1.0   3261  200.477461   108.0      13.0     4726.0  316.956115   \n",
       "4       1.0   3240  168.157716    95.0      13.0     2741.0  233.959012   \n",
       "\n",
       "   Magic1      Magic2  Magic3  Magic4   Magic5      Magic6  Magic7  \\\n",
       "0   66360  215.997107   134.0    13.0   9532.0  289.687066   18103   \n",
       "1  134451  318.114131   161.0    13.0  13150.0  464.342597   36987   \n",
       "2  134451  318.114131   161.0    13.0  13150.0  464.342597    6653   \n",
       "3   81986  203.351487   122.0    13.0   6426.0  263.878412    7575   \n",
       "4   81986  203.351487   122.0    13.0   6426.0  263.878412   17767   \n",
       "\n",
       "       Magic8  Magic9  Magic10  Magic11     Magic12  \n",
       "0  313.646523   148.0     13.0  10544.0  473.337118  \n",
       "1  222.396247   122.0     13.0   9532.0  329.431315  \n",
       "2  160.297760    94.0     13.0   4009.0  224.186851  \n",
       "3  244.712739   149.0     13.0   4726.0  298.571900  \n",
       "4  184.067372   107.0     13.0   5334.0  252.308016  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option(\"display.max_columns\", 100)\n",
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3fda275c-e7af-426c-a78d-4123358e386a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([62, 41, 574, 12, 1], dtype='int64', name='Operational_Region_Coverage_Area')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.Operational_Region_Coverage_Area.value_counts()[:5].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "468a3ab6-feae-4ee3-a227-9f5f9d138a82",
   "metadata": {},
   "outputs": [],
   "source": [
    "top5 = [62, 41, 574, 12, 1]\n",
    "\n",
    "def convert(x):\n",
    "    if x in top5:\n",
    "        return(\"top5\")\n",
    "    else:\n",
    "        return(\"Others\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2fd73dc6-fb1d-4431-8528-2ba9bcdebd7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"area\"] = combined.Operational_Region_Coverage_Area.apply(convert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "16e5b2ff-ddce-4415-b7d7-5d1ed2146c88",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined.drop('Operational_Region_Coverage_Area', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "033fd072-92aa-4583-93e4-92491085df46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"Custom_Promoted\", y = \"Orders_Count\", data=combined)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8846a57d-474c-4130-b043-eb4d3de2d587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"Search_Promotions\", y = \"Orders_Count\", data=combined)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "61632c2c-b330-4d16-bb3a-7cec82b5626d",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"Promotions\"] = combined.Custom_Promoted+combined.Promoted+combined.Search_Promotions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "01b934c2-d90c-4a65-a383-667db8a2d67b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2DPfee6/484IFCwAA06dPR3p6Op599lnU1NRg9uzZqKysRFRUFPbs2QNPT0/xd1avXg0nJydMnjwZNTU1GDVqFNLT03X2k8nMzMT8+fPFVXYTJkzQ2RvK0dERn376KWbPno2YmBi4u7sjPj4er732mrn/CYiIbE5xcbHBggm4dbBpcXExwsLCLJsUkQ1QCbw43WGknpJMRGTLBEHAwoULcezYMZ1LdCqVCkOGDMGKFSvaXJBD9ik7Oxtr1qxBUlKSouYtSv38ttk5TUREZB0qlQrPPPMMHBx0PyIcHR3xzDPPsGCSKe7L1TYWTUREpIebmSpPRkYGLl++DAC4fPky9+UygEUTEREZxM1MlaO0tBSZmZni5VhBEJCZmcl9uZph0URERAZxM1NlaNqXq6V2Tn3+jVVXzxERkW2LiYlR1IRgJWral6u5xsZGcV8urpa8hSNNRNQu2dnZmDRpErKzs62dChF1gNDQUAwZMkRnqx7g1sT/oUOHIjQ01EqZ2R4WTUQkGVfXEMlP02rJltq5WvI3LJqISDKuriGSJ66WlIZFExFJwtU1RPLG1ZJtY9FERG1qWkWj1Wp12hsbG7m6hkgmuFqybSyaiKhNPPVeuTjxX1liYmLwwQcfcMVkC1g0EVGbmk69N4Sn3ssXJ/4T6WLRRCbjN1H5k3LqPckPJ/4T6WLRRCbhN1FlaNrHpfnSY5VKxX1cZIoT/5WJX4Jbx6KJTMJvosrQ0n4tDg4O3MdFhnishjLV1tYiNTUVFRUVSE1N5ZdgA1g0kdH4TVRZgoKCMG3aNJ22adOmcR8XGWqa+N/Y2KjTfvuxGiQ/6enp4mX4qqoqbNy40coZ2R4WTWQUfhNVpmnTpsHX1xcA0K1bN+7jIlM8VkN5SktLsWXLFp22LVu28EtwMyyayCj8JqpM3MdFGXishrIIgoBXX31V78uuVqs12K5kLJrIKPwmqlzcx0UZeKyGchQWFiIvL89gLC8vD4WFhZZNyIaxaCKj8JsokfzxWA0iXSyayGj8JqpMXJKsHLwcqwxhYWG44447DMb69OmDsLAwyyZkw1g0kUn4TVRZuC+X8vByrDK0VBC7urpaOBPbxqKJTMJvosrCfbmI5KeoqKjVOU1c2PMbFk1kMn4TVQbuy0UkT00LexwcdEsCLuzRx6KJiNrEfbmI5KulBTxc2KOPRRMRtYn7cinX+vXrMXLkSKxfv97aqZAZcWGPNCyaiKhN3JdLma5evYqMjAxotVpkZGTg6tWr1k6JzIgLe9rGoomI2sR9uZTphRdegFarBXBrd+i///3vVs6IzIkLe9rGoomIJOHwvbIcO3YMJ0+e1GnLy8vDsWPHrJQRWQIX9rSORRMRScbhe2XQarVISUkxGEtJSRFHn4iUhkUTEUnG4XtlOHToEKqqqgzGqqqqcOjQIQtnRGQbnKydABHZl5iYGA7dy1x0dDS8vLwMFk5qtRrR0dFWyIrI+jjSREREOhwcHFq8PLdkyRK9TRCJlIL/zyeiduGBvcowePBgDBgwQKdt4MCBuPvuu62UEZH1sWgiIsl4YK+yvPLKK+KokoODA/75z39aOSMyN34pap1RRdPSpUtx48YNvfaamhosXbrU5KSIyDbxwF5l6dKlC0aMGAEAGDFiBLp06WLdhMis+KWobUYVTUuWLEF1dbVe+40bN7BkyRKTkyIi28MDe5WntrZW3Kvp5MmT/BCVOX4paptRRZMgCAZ3AP7+++/h7e1tclJEZFt4YK8yZWRk4NKlSwCAS5cu8UNUxvilSJp2FU1du3aFt7c3VCoV7rjjDnh7e4s3tVqNMWPGYPLkyebKlYishAf2Kk9paSkyMjJ02jIyMvghKkP8UiRdu/ZpWrNmDQRBwGOPPYYlS5ZArVaLMRcXF4SFhXH/DiIZajqw98SJEzqFk6OjIyIjI3lgr8y09GGp1WqxevVqvPbaazxvUEaavhQ1d/uXorCwMMsnZoPaNdI0ffp0zJgxA19//TWefvppTJ8+XbxNnTrVLAXTzZs38fe//x09e/aEu7s7evXqhaVLl+ps4y8IAlJSUhAYGAh3d3eMHDkSp0+f1rmfuro6zJs3D76+vvDw8MCECRP0vjFVVlYiMTERarUaarUaiYmJPNWbCDywV2maPkSbF02CIHBkUYaavhQ5OjrqtDs6OmLo0KH8UnQbo+Y0jRgxAo6Ojvjpp59w4MAB7N+/X+fWkf71r3/hrbfeQlpaGs6cOYPly5djxYoVWLt2rdhn+fLlWLVqFdLS0nD06FEEBARgzJgxuHbtmtgnKSkJO3fuxNatW3HgwAFUV1cjLi5O51tzfHw8cnNzkZWVhaysLOTm5iIxMbFDnw+RveKBvcoREhICLy8vgzEvLy+EhIRYOCMyJ34pkk4lGHGxMicnB/Hx8SgqKtL7JqJSqfTmPZgiLi4O/v7+ePfdd8W2hx56CJ06dcJ7770HQRAQGBiIpKQkPPfccwBujSr5+/vjX//6F5588kloNBp069YN7733HqZMmQIAOH/+PIKDg7F7926MGzcOZ86cQb9+/ZCTk4OoqCjxeUZHR+PHH39Enz592sy1qqoKarUaGo2mxT84RPbs6tWrmDhxIrRaLRwcHLBr1y4uQ5ehwsJCPPLIIy3GN23axMs1MvTWW29h8+bN4s8JCQl48sknrZiR5Uj9/DZqpOmpp57C4MGDcerUKVy5cgWVlZXi7cqVK0Ynbcjvf/97fPnll/jpp58A3Fqhd+DAAfzxj38EABQUFKC8vBxjx44Vf8fV1RUjRozAwYMHAQDHjx9HQ0ODTp/AwEBERESIfQ4dOgS1Wi0WTAAwbNgwqNVqsU9zdXV1qKqq0rkRydmHH34oXhrXarXYvn27lTMicwgNDdXbDbzJwIEDebmGFMuoA3t//vlnfPjhh+jdu3dH56Pnueeeg0ajwe9+9zs4OjqisbERr7zyCqZOnQoAKC8vBwD4+/vr/J6/v7943b28vBwuLi7o2rWrXp+m3y8vL4efn5/e4/v5+Yl9mktNTeW+VKQYTUuSb5eZmYlx48YhKCjISlmRudTX1xtsr6urs3AmZAmlpaXYtm2bTtu2bdswfvx4vr9vY9RIU1RUFM6ePdvRuRi0bds2ZGRkYPPmzThx4gQ2btyI1157DRs3btTp1/yaa0t7SbXWx1D/1u5n8eLF0Gg04q2kpETq0yKyK1ySrCyFhYXIz883GMvPz0dhYaFlEyKzamu1JN/fvzFqpGnevHlITk5GeXk5BgwYAGdnZ534wIEDOyQ5APjb3/6GRYsW4S9/+QsAYMCAASgqKkJqaiqmT5+OgIAAALdGirp37y7+3oULF8TRp4CAANTX16OyslJntOnChQsYPny42KeiokLv8S9evKg3itXE1dUVrq6uHfNEiWwYlyQTyVdL72+tVsv3dzNGjTQ99NBDOHPmDB577DEMGTIEd955J+666y7xfzvSjRs3xAMjmzg6OorzKnr27ImAgADs3btXjNfX12Pfvn1iQRQZGQlnZ2edPmVlZTh16pTYJzo6GhqNBkeOHBH7HD58GBqNRuxDpFRNS5KbvxcdHBy4JFmGwsLCWp3TxA9QeeEcNumMGmkqKCjo6Dxa9MADD+CVV15BSEgI+vfvj++++w6rVq3CY489BuDWJbWkpCQsW7YM4eHhCA8Px7Jly9CpUyfEx8cDANRqNWbOnInk5GT4+PjA29sbCxcuxIABAzB69GgAQN++fREbG4tZs2Zh3bp1AIAnnngCcXFxklbOEclZ09LjhIQEnXZBELgkWYZUKhUWL16MhIQEnUszTe18veWHr6k0RhVNlqw6165dixdffBGzZ8/GhQsXEBgYiCeffBIvvfSS2OfZZ59FTU0NZs+ejcrKSkRFRWHPnj3w9PQU+6xevRpOTk6YPHkyampqMGrUKKSnp+ts5pWZmYn58+eLq+wmTJiAtLQ0iz1XIltnaLNDzneQp6CgIEydOlVnCXp8fDz35ZKhoqIi5OXlGYzl5eXx8txtjNqnadOmTa3GW9vfQ864TxPJlSAImDdvnsE/rAMHDsTatWv5TVWGamtr8dBDD+HatWvw8vLChx9+CDc3N2unRR1Mq9ViwoQJBrfN8fLywscff6x3aV5upH5+GzXS9Ne//lXn54aGBty4cQMuLi7o1KmTYosmIrkqLCxs9ZtoYWEhevbsaeGsyNzc3Nzw/PPPY82aNUhKSmLBJFPFxcUt7jNYVVWF4uJijjT9j1FFU2VlpV7bzz//jKeffhp/+9vfTE6KiIhsQ0xMDGJiYqydBplR00KPY8eO6c1hGzJkCCeC36bDxtvCw8Px6quv6o1CEZH942oqIvlqWuhhaKU6F3ro6tCLlI6Ojjh//nxH3iUR2YCWVk1xNRWRPPBAbmmMujz38ccf6/wsCALKysqQlpbGYVwimeJqKiJ5mzZtGnbv3o1Lly7B19dXb4sRMnL1XPMhPJVKhW7duuG+++7DypUrdXbmVhKuniO542oqInnLzs4WJ/4raRBE6ue3UUUTGcaiiZRAqX9UlYqvNymB1M9vk+c0cXM7ImWJiYnBBx98wA9QBaitrcXKlStRUVGBlStXora21topkZllZ2dj0qRJyM7OtnYqNsnoomnTpk0YMGAA3N3d4e7ujoEDB+K9997ryNyIiMiKMjIycPnyZQDA5cuXkZmZaeWMyJxYJLfNqKJp1apVePrpp/HHP/4R77//PrZt24bY2Fg89dRTWL16dUfnSEQ2hN9ElaG0tBSZmZnilQRBEJCZmYnS0lIrZ0bmwiK5bUbNaerZsyeWLFmit/P3xo0bkZKSYtEDfW0J5zSR3N0+EdzT0xPbt2/nRHAZEgQBCxcuxIkTJ9DY2Ci2Ozo64u6778Zrr73GbSZkprS0FImJiTqvt5OTEzZt2oSgoCArZmYZZp3TVFZWhuHDh+u1Dx8+HGVlZcbcJRHZgfT0dFy7dg0AcO3aNWzcuNHKGZE5FBUV4ejRozofoADQ2NiIo0ePoqioyEqZkTkIgmDwKlFTO+ct/8aooql37954//339dq3bduG8PBwk5MiIttTWlqqs0cTAGzevJmXa2So6VgNQztEDx06lMdqyAyLZOmMKpqWLFmCl156CbGxsfjHP/6Bf/7zn4iNjcWSJUuwdOnSjs6RbBznuMifIAhITU1tsZ3fROWl6VgNrVar067Vanmshgw1FcmGdvxnkazLqKLpoYcewuHDh+Hr64tdu3Zhx44d8PX1xZEjR/CnP/2po3MkG8bVFspQWFiIkydPGoydPHkShYWFlk2IrIJbzMiTSqXC1KlT9V5bQRAwdepUFsm3MXrLgcjISGRkZOD48eM4ceIEMjIycNddd3VkbmQHuNqCSH5aGlkEwJFFGRIEAenp6QZjGzZs4Ot9m3YVTefPn8fChQtRVVWlF9NoNPjb3/6GioqKDkuObBuXJBPJE0cWlaWwsBB5eXkGY3l5eXy9b9OuomnVqlWoqqoyuBxPrVbj2rVrWLVqVYclR7aLqy2UJTQ0FJ07dzYY69y5M+c8EJEitKtoysrK0tub6XaPPPIIPvnkE5OTItvH1RbKUlxcjOrqaoOx6upqFBcXWzgjMqewsDAMHDjQYGzQoEEICwuzbEJkVmFhYRgwYIDB2MCBA/l636ZdRVNBQQFCQkJajAcFBXEYTyGaVls4OjrqtHNJsjw1vd6G8PWWH5VKhRkzZhiMzZgxgxODZUalUmHx4sUGV88ZaleydhVN7u7urRZFhYWFcHd3NzUnsgNNS5JbauebTF6aXldD+/bw9ZYfQRCwZcsWg7HNmzfz8rsMBQUFYerUqTpt8fHx6NGjh5Uysk3tKpqioqJaPZR306ZNGDp0qMlJkX0ICgpCQkKC+IGpUqmQkJDAN5lMBQUFoX///jpt/fv35+stQ02X3w3h5Xf5mjFjhjjw4e7ujunTp1s5I9vTrqJp4cKF2LBhAxYuXKizSq6iogLJyclIT0/HwoULOzxJsl3Tpk2Dj48PAMDX1xcJCQlWzojMpbS0FKdPn9ZpO336NFdLylBoaGirc1x4OVa+nJ2ddf6XdLWraLr33nvxn//8B2lpaQgMDETXrl3h7e2NwMBA/Oc//8HatWtx3333mStXskFubm5ITk6Gv78/FixYwMNbZappVaShy3BcLSlPvOSqPBkZGTpnS3LfPX0qwYi/dufOncP777+Ps2fPQhAE3HHHHXj44YcVcRJya6SekkxkbwoLC1tdObtp0yausJERvt7KU1paisTERJ0V0U5OTti0aZMiPtulfn47GXPnPXr0MDgJuLnx48dj/fr16N69uzEPQ0Q2omn13PHjx3XOI3NwcMDgwYN5uUZmQkNDMXDgQIMbHvLynPw0jSQ3P2uwsbERq1evxmuvvcaRx/8x+hgVKfbv34+amhpzPgQRWUDT6jlDZ1Nx9Zw81dXVtaud7FfTxH9D729O/Ndl1qKJlCE7OxuTJk1Cdna2tVMhC1OpVJzPJEOFhYXIz883GMvPz+d+fDITEhLS6o7/re3PqDQsmsgktbW1WLlyJSoqKrBy5UrU1tZaOyUyg6bh++b7NKlUKk4EJ7JzRUVFre74z5Gm37BoIpNkZGTg8uXLAIDLly9ztYVM8dgcZeGxGkSGsWgio5WWliIzM1McZRAEAZmZmdy3R4Z4bI6yqFQqPProowZjjz76KOewyUxoaCg6depkMNapUye+v2/DoomM0nS5pqV2Xq6RFx6boyyCICA9Pd1gbMOGDXx/y0xRURFu3LhhMHbjxg2OJN/GrEXT888/D29vb3M+BFkJL9coT1BQEKZMmaLTNmXKFB6jIkOFhYUGtxsAgLy8PE4EJ8UyqmjauHEjPv30U/HnZ599Fl26dMHw4cN1PiwXL16MLl26mJwk2R5eriEikoewsDAMHDjQYGzQoEGcw3Ybo4qmZcuWiYf6HTp0CGlpaVi+fDl8fX0lbXpJ9o+Xa5SntLQU27Zt02nbtm0b57DJUEhIiN5KySYODg5cgi4zKpUKixYt0vu77eDgYLBdyYwqmkpKStC7d28AwK5du/Dwww/jiSeeQGpqKr799tsOTZBsV1BQEBISEsQ3lEqlQkJCAi/XyBDnsClLTk6O3u7QTbRaLXJyciycEZlbUFAQpk6dqtM2depU/j1vxqiiqXPnzuIy8z179mD06NEAbh3eyh3AlWXatGnw8fEBAPj6+iIhIcHKGZE5cA6bsrR19BWPxpKnGTNmiOeuqdVqTJ8+3coZ2R6jiqYxY8bg8ccfx+OPP46ffvoJ48ePBwCcPn2a1z4Vxs3NDcnJyfD398eCBQvg5uZm7ZTIDJrOIjOEZ5ERyYObmxsWL14Mf39/LFq0iH/PDTDqwN7//Oc/ePHFF1FcXIzt27eLIw3Hjx/XG94j+YuJiUFMTIy10yAza+kSHC/NyU9brylfc/ni3/PWtbtounnzJv7973/j2WefRXBwsE5syZIlHZYYEdmOoqIinDx50mDs5MmTKCoq4iizjJSVlbUZ/7//+z8LZUOWlJ2djTVr1iApKYnFkwHtvjzn5OSEFStW6M1tMKdz586Jc2c6deqEO++8E8ePHxfjgiAgJSUFgYGBcHd3x8iRI3H69Gmd+6irq8O8efPg6+sLDw8PTJgwQW/VT2VlJRITE6FWq6FWq5GYmIirV69a4ikS2bTQ0NBWj9Xg5Tl54ZwmZaqtrcXSpUtRUVGBpUuX8ixRA4ya0zR69Gh88803HZyKYZWVlYiJiYGzszM+++wz/PDDD1i5cqXO/k/Lly/HqlWrkJaWhqNHjyIgIABjxozBtWvXxD5JSUnYuXMntm7digMHDqC6uhpxcXE6xV98fDxyc3ORlZWFrKws5ObmIjEx0SLPk8jWcdmxckgZaSL5Wb9+vbiYq6amBu+++66VM7I9KsGIi9Pr1q1DSkoKEhISEBkZCQ8PD534hAkTOizBRYsWITs7u8WtDARBQGBgIJKSkvDcc88BuDWq5O/vj3/961948sknodFo0K1bN7z33nvijsbnz59HcHAwdu/ejXHjxuHMmTPo168fcnJyEBUVBeDWstvo6Gj8+OOP6NOnT5u5VlVVQa1WQ6PRiCsQiOSgsLAQjzzySIvxTZs28fKcjDQ2NmLUqFEGtx1wcHDAl19+qbexLdm30tJSxMfH67Vv3rwZQUFBVsjIsqR+fhs10vT000+joqICq1atQkJCAiZOnCje/vSnPxmdtCEff/wxBg8ejEmTJsHPzw933XUX3nnnHTFeUFCA8vJyjB07VmxzdXXFiBEjcPDgQQC3Jqg3NDTo9AkMDERERITY59ChQ1Cr1WLBBADDhg2DWq0W+zRXV1eHqqoqnRuRHDXtAN98tEmlUnEHeBkqKSlpdZ+mkpISC2dE5iQIQotzkpcsWcKJ/7cxqmjSarUt3jp6rtOvv/6KN998E+Hh4fj888/x1FNPYf78+di0aRMAoLy8HADg7++v83v+/v5irLy8HC4uLujatWurffz8/PQe38/PT+zTXGpqqjj/Sa1W602MJ5ILlUqFqVOn6v3xFAQBU6dO5aU7mQkJCUHnzp0Nxjp37swdwWWmoKAA+fn5BmP5+fkoKCiwcEa2y+QDe809UUyr1eLuu+/GsmXLcNddd+HJJ5/ErFmz8Oabb+r0a/5HWxCENv+QN+9jqH9r97N48WJoNBrxxm9fJFeCIGDLli0GY5s3b+Y3UZkpKipCdXW1wVh1dTU3M5WZc+fOmRRXEqOKpsbGRvzjH/9Ajx490LlzZ/z6668AgBdffLHDJ451794d/fr102nr27cviouLAQABAQEAoDcadOHCBXH0KSAgAPX19aisrGy1T0VFhd7jX7x4UW8Uq4mrqyu8vLx0bkRy1LQjuCHcEZyIlMKooumVV15Beno6li9fDhcXF7F9wIABWL9+fYclB9zaaKv5sOFPP/0kzqHo2bMnAgICsHfvXjFeX1+Pffv2Yfjw4QCAyMhIODs76/QpKyvDqVOnxD7R0dHQaDQ4cuSI2Ofw4cPQaDRiHyKl4pYDytLWNAtLbjlD5hcYGGhSXEmMKpo2bdqEt99+GwkJCTorKAYOHIgff/yxw5IDgGeeeQY5OTlYtmwZzp49i82bN+Ptt9/GnDlzANy6pJaUlIRly5Zh586dOHXqFGbMmIFOnTqJKwHUajVmzpyJ5ORkfPnll/juu+8wbdo0DBgwQDw3r2/fvoiNjcWsWbOQk5ODnJwczJo1C3FxcZJWzhHJHectKUdeXp5JcbIvDg6tlwJtxZXEqGNUzp07h969e+u1a7VaNDQ0mJzU7YYMGYKdO3di8eLFWLp0KXr27Ik1a9boHAz77LPPoqamBrNnz0ZlZSWioqKwZ88eeHp6in1Wr14NJycnTJ48GTU1NRg1ahTS09N1ir7MzEzMnz9fXGU3YcIEpKWldejzIbJHRUVFLX5Q5uXlcUdwmWk6GsvYONmXsLAwDBw40OB7fNCgQXxv38aooql///749ttv9YbkP/jgA9x1110dktjt4uLiEBcX12JcpVIhJSUFKSkpLfZxc3PD2rVrsXbt2hb7eHt7IyMjw5RUiWSpaTWVocnBXE0lP229nny95UWlUmHGjBlYsGCBXmzGjBkcZb6NUUXTyy+/jMTERJw7dw5arRY7duxAfn4+Nm3ahE8++aSjcyQiK5Oymqpnz54WzorMJTQ0FE5OTrh586ZezMnJiXPYZKat1bF33303C6f/MepC5QMPPIBt27Zh9+7dUKlUeOmll3DmzBn897//xZgxYzo6RyIisqBff/3VYMEE3Dq0vWnFNMkDV8dKZ/TsrnHjxmHfvn2orq7GjRs3cODAAZ0dt4lIPkJDQ+Hm5mYw5ubmxpEHmdm5c6dJcbIv3PFfOk6JJ6I2FRYWtriRbW1tLQoLCy2bEJnVvHnzTIqTfeGO/9JJLpq6du0Kb29vSTcikheeeq8sn376qUlxsi9Nc5oMjTRxx39dkieCr1mzRvzvy5cv45///CfGjRuH6OhoALcOvP3888/x4osvdniSRGRd0dHRra6ea/o7QPLQ0ikIUuNkX1qa0yQIgjinidsO3CK5aJo+fbr43w899BCWLl2KuXPnim3z589HWloavvjiCzzzzDMdmyURWZWDgwPmzZuH1NRUvdhf//pXbn4nM1FRUSbFyb6Ehoa2uE8Td/zXZdRfus8//xyxsbF67ePGjcMXX3xhclJEZFsEQWjxvb1nzx4O38tMW1vHcGsZ+amrq2tXu1IZVTT5+PgYXD2xa9cu7hRLJENckqwsERERJsXJvhQWFuqd8dokPz+fCz1uY9TmlkuWLMHMmTPxzTffiHMZcnJykJWV1eEH9hKR9TUtST5x4oTOYa2Ojo6IjIzk8L3MlJeXtxkPDw+3UDZEtsOokaYZM2bg4MGD6NKlC3bs2IHt27dDrVYjOzsbM2bM6OAUicjaVCqVwbmKTe1ckiwvtxfGxsTJvoSGhqJz584GY507d+aXotu0u2hqaGjAo48+im7duiEzMxMnTpzAd999h8zMTE4OJJKxoKAg9OvXT6etX79+6NGjh5UyInM5cuSISXGyL8XFxa0ek1RcXGzhjGxXu4smZ2dn7gZLpEClpaU4deqUTtupU6dQWlpqpYzIXNo6R5DnDMpLSEgIvLy8DMa8vLx4QPNtjLo896c//Qm7du3q4FSIyFYJgoDVq1e32M7Vc/Ly5z//2aQ42Zfi4mJUVVUZjFVVVXGk6TZGTQTv3bs3/vGPfyA7OxuDBw+Gh4eHTnz+/PkdkhwR2QZufqcsbRXBLJLlJSQkpNXNaznS9Bujiqb169ejS5cuOHHiBE6cOKETU6lULJqIZKZp+N7Qt1EO38vPhg0b2ozPmjXLQtmQuRUVFbU6p6moqIiXZP/HqKKpoKAAAHDp0iWoVCruzUQkc1KG7znSJB9tbSfA7QbkhSOL0rV7TtPVq1cxZ84c+Pr6wt/fH35+fvD19cXcuXOh0WjMkSMRWVnTPk2GDvQcOnQolyTLzKVLl0yKk31pa8sQbinym3aNNF25cgXR0dE4d+4cEhIS0LdvXwiCgDNnziA9PR1ffvklDh48iK5du5orXyKygqb9mBISEvRi3KdJfu68806T4mRfwsLCWjx7btCgQRxFvk27iqalS5fCxcUFv/zyi94p10uXLsXYsWOxdOlSg6tsiMj+NR+mFwSBQ/cyJGVH8N69e1soGzI3lUqFRYsWIT4+Xi+2aNEifim6Tbsuz+3atQuvvfaaXsEEAAEBAVi+fDn3cCKSoZa2HADALQdkiDuCK5Ohy+98b+tqV9FUVlaG/v37txiPiIho8xsKEdkfHtirLA4OrX80tBUn+9LSfmvch01fu/6f7+vr2+ppxwUFBVxJRyRDwcHBLX5QOjg4IDg42MIZkTlptVqT4mRf+KVIunYVTbGxsXjhhRdQX1+vF6urq8OLL76I2NjYDkuOiGzDoUOHWvyg1Gq1OHTokIUzInO6ePGiSXGyL8HBwXB0dDQYc3R05Jei27RrIviSJUswePBghIeHY86cOfjd734HAPjhhx/wxhtvoK6uDu+9955ZEiUi6wkMDDQpTvbF19fXpDjZl5ycnBbnqTU2NiInJwcxMTEWzso2tatoCgoKwqFDhzB79mwsXrxYvM6pUqkwZswYpKWlsSIlIrJzly9fNilO9iU6Ohpubm6ora3Vi7m5uSE6OtoKWdmmdu8I3rNnT3z22WeorKzEzz//DODWWXTe3t4dnhwR2QZufqcs3bp1MylO9kUQBNTV1RmM1dXVcSL4bYxeAtG1a1cMHToUQ4cOZcGkcOvXr8fIkSOxfv16a6dCZhISEtLqRHCePScv3bt3NylO9uWjjz5qsTASBAEfffSRhTOyXVw3Sia5evUqMjIyoNVqkZGRgatXr1o7JTKDnJycVieC5+TkWDgjMqevv/7apDjZF66WlI5FE5nkhRdeEN9QWq0Wf//7362cEZnDsGHDWl1dM2zYMAtnRObk5eVlUpzsy1133WVSXElYNJHRjh07hpMnT+q05eXl4dixY1bKiMyluLi41dU1xcXFFs6IzKn5+7q9cbIvvXr1Qo8ePQzGgoKC0KtXLwtnZLtYNJFRtFotUlJSDMZSUlI4nEtkx3hgr/J07tzZYLuHh4eFM7FtLJrIKIcOHUJVVZXBWFVVFTc7lJm2thLhViPyEhERYVKc7EthYSHy8/MNxvLz81s9CURpWDSRUaKjo1uc16BWq7mvh8x8/PHHJsXJvmzdutWkONmXtrYU4JYDv2HRREZxcHDAnDlzDMbmzp3LAz1lhvv2KMvzzz9vUpxIrvjJRkYRBAGffvqpwdh///tffjORmbZWx3H1nLycP3/epDiRXLFoIqMUFhYiLy/PYCwvL4/XwGWGl+eUpaSkxKQ4kVyxaCKiNvn4+JgUJ/vCY3OUpbS01KS4krBoIqOEhYWhT58+BmN9+vRBWFiYZRMis2o6Z9LYONmXlvbkkhon+3Lx4kWT4krCoomM5uLi0q52sl8jRowwKU72paXl51LjZF+4L5d0dlU0paamQqVSISkpSWwTBAEpKSkIDAyEu7s7Ro4cidOnT+v8Xl1dHebNmwdfX194eHhgwoQJesONlZWVSExMhFqthlqtRmJiIs9Ra0VRUVGLuwKfPHkSRUVFFs6IzOnUqVMmxcm+cKRJWcLCwlq85KpSqXjl4DZ2UzQdPXoUb7/9NgYOHKjTvnz5cqxatQppaWk4evQoAgICMGbMGFy7dk3sk5SUhJ07d2Lr1q04cOAAqqurERcXp/PGj4+PR25uLrKyspCVlYXc3FwkJiZa7PnZm9DQUAwZMkRvawEHBwcMHToUoaGhVsqMzKH5+669cbIvnNOkLDk5OS2ueBYEgQdy38Yuiqbq6mokJCTgnXfeQdeuXcV2QRCwZs0avPDCC/jzn/+MiIgIbNy4ETdu3MDmzZsBABqNBu+++y5WrlyJ0aNH46677kJGRgZOnjyJL774AgBw5swZZGVlYf369YiOjkZ0dDTeeecdfPLJJxyGboFKpcIzzzyj98fTwcHBYDvZNy5BVxaeeq8sQ4YMMSmuJHZRNM2ZMwfjx4/H6NGjddoLCgpQXl6OsWPHim2urq4YMWIEDh48CAA4fvw4GhoadPoEBgYiIiJC7HPo0CGo1WpERUWJfYYNGwa1Wi32MaSurg5VVVU6NyUJCgpCQkKCWCCpVCokJCS0ePAj2a8LFy6YFCf78v3335sUJ/uyceNGk+JKYvNF09atW3HixAmkpqbqxcrLywEA/v7+Ou3+/v5irLy8HC4uLjojVIb6+Pn56d2/n5+f2MeQ1NRUcQ6UWq1W5Plb06ZNE5eb+/r6IiEhwcoZkTkYen+0J072pa0vgEr7gih3Xbp0MSmuJDZdNJWUlOCvf/0rMjIy4Obm1mK/5peCBEFo8/JQ8z6G+rd1P4sXL4ZGoxFvStzwzc3NDcnJyfD398eCBQtafZ3IfvFsKmXp1auXSXGyL80HHtobVxKbLpqOHz+OCxcuIDIyEk5OTnBycsK+ffvw+uuvw8nJSXwhm48GXbhwQYwFBASgvr4elZWVrfapqKjQe/yLFy+2+n8WV1dXeHl56dyUKCYmBh988AFiYmKsnQqZyQ8//GBSnOxLQECASXGyL/xSJJ1NF02jRo3CyZMnkZubK94GDx6MhIQE5ObmolevXggICMDevXvF36mvr8e+ffswfPhwAEBkZCScnZ11+pSVleHUqVNin+joaGg0Ghw5ckTsc/jwYWg0GrEPtSw7OxuTJk1Cdna2tVMhM+HlGmUpKCgwKU72hXMWpXOydgKt8fT0REREhE6bh4cHfHx8xPakpCQsW7YM4eHhCA8Px7Jly9CpUyfEx8cDANRqNWbOnInk5GT4+PjA29sbCxcuxIABA8SJ5X379kVsbCxmzZqFdevWAQCeeOIJxMXFtbjrNd1SW1uL1NRUVFVVITU1FR9++CEv0cnQsGHDWjyguSlORPaJI03S2XTRJMWzzz6LmpoazJ49G5WVlYiKisKePXvg6ekp9lm9ejWcnJwwefJk1NTUYNSoUUhPT4ejo6PYJzMzE/PnzxdX2U2YMAFpaWkWfz72Jj09XRxlqKqqwsaNG/Hkk09aOSvqaIGBgSbFyb788ssvJsXJvnBfLunsrmj65ptvdH5WqVRISUlBSkpKi7/j5uaGtWvXYu3atS328fb2RkZGRgdlqQylpaXYsmWLTtvmzZsxfvx4BAUFWSkrMgcpS9DDw8MtlA0RdSSujpXOpuc0ke0SBAGvvvqq3rBtS+1k37jZobI4OzubFCf7wgN7pWPRREYpLCxEXl6ewVheXh4KCwstmxCZ1ZUrV0yKk3158MEHTYqTfenWrZtJcSVh0URG4cRBZWm+OWx742Rfjh49alKc7MulS5dMiisJiyYyCicOKguH75VFo9GYFCf7MmjQIJPiSsKiiYwSFhbW4nYMv/vd7xAWFmbZhMisfv31V5PiZF8cHFr/aGgrTvbl3LlzJsWVhP/PJ6O5uLgYbOckUfkZOnSoSXGyL5wIriw5OTkmxZWERRMZpaioCCdPnjQYO3nyJIqKiiycEZkTj9VQlpa+EEmNk32JiooyKa4kLJrIKMHBwTqbg97O0dERwcHBFs6IzCk/P9+kONmX0tJSk+JkX3iMinQsmsgoOTk5aGxsNBhrbGzkcK7MqNVqk+JkX5ycWt/3uK042Zeff/7ZpLiSsGgiowwbNqzFkSYnJyeeRSYzlZWVJsXJvtTU1JgUJ/vC1bHSsWgio5SUlLQ40nTz5k2UlJRYOCMypx9//NGkONkXnjWoLDxGRToWTWSU0NBQDBkyxGBs6NChCA0NtXBGZE7+/v4mxcm+nD171qQ42Ze2rgzwysFvWDSRUVQqFaZOnWowNnXqVG5uKTM9e/Y0KU72pXv37ibFyb6wSJaORRMZRRAErF271mDs9ddf5zEqMnP16lWT4mRfGhoaTIqTfeFCD+lYNJFRCgoKUFBQ0O4Y2SfOaVIWLkFXlps3b5oUVxIWTWSU7777zqQ42Reegq4s3HJAWb788kuT4krCoomMcuedd5oUJ/tSXl5uUpzsCyf+K8t9991nUlxJWDSRUXigp7Jw3x5luXz5sklxsi98f0vHTzYySo8ePUyKk33hHBdlaWv1K1fHyss999xjUlxJWDSRUV5//XWT4mRfamtrTYqTfeHIg7Lk5uaaFFcSFk1klMjISJPiZF9CQkJMihOR7dq9e7dJcSVh0URG4eUaZXFxcTEpTkS2y9PT06S4krBoIqN8//33JsXJvvz0008mxYnIdvXp08ekuJKwaCKiNrV0OLPUONkXjjwoC78USceiiYzCfVyUJTw83KQ42Zfq6mqT4mRfHB0dTYorCYsmMkpRUZFJcbIvrq6uJsXJvnTp0sWkONmX3r17mxRXEhZNZJT777/fpDjZF+7boyyVlZUmxcm+8EBu6Vg0kVH4JlOWn3/+2aQ4Edku7gAvHYsmMgq3HFAWnoJOJF+c+C8diyYyCk/FVpZu3bqZFCci25WXl2dSXElYNJFRBEEwKU725dy5cybFich2ubm5mRRXEhZNZBStVmtSnIiIbENERIRJcSVh0URG4b4eRETywC0mpGPRREapqqoyKU5ERLZh//79JsWVhEUTGYVzmoiI5IGb10rHoomIiEjBxowZY1JcSVg0kVF4gCsRkTwcP37cpLiSsGgio3D1HBGRPFy5csWkuJKwaCIiIlKwyMhIk+JKwqKJiIhIwXi2pHQ2XzSlpqZiyJAh8PT0hJ+fHyZOnIj8/HydPoIgICUlBYGBgXB3d8fIkSNx+vRpnT51dXWYN28efH194eHhgQkTJqC0tFSnT2VlJRITE6FWq6FWq5GYmMiDZ4mISNbOnDljUlxJbL5o2rdvH+bMmYOcnBzs3bsXN2/exNixY3H9+nWxz/Lly7Fq1SqkpaXh6NGjCAgIwJgxY3Dt2jWxT1JSEnbu3ImtW7fiwIEDqK6uRlxcnM6E5fj4eOTm5iIrKwtZWVnIzc1FYmKiRZ8vERGRJfn7+5sUVxInayfQlqysLJ2fN2zYAD8/Pxw/fhz33HMPBEHAmjVr8MILL+DPf/4zAGDjxo3w9/fH5s2b8eSTT0Kj0eDdd9/Fe++9h9GjRwMAMjIyEBwcjC+++ALjxo3DmTNnkJWVhZycHERFRQEA3nnnHURHRyM/Px99+vSx7BMnIiKyAJ4tKZ3NjzQ1p9FoAADe3t4AgIKCApSXl2Ps2LFiH1dXV4wYMQIHDx4EcGu5ZENDg06fwMBAREREiH0OHToEtVotFkwAMGzYMKjVarFPc3V1daiqqtK5ERER2RMnp9bHT9qKK4ldFU2CIGDBggX4/e9/Lx4gWF5eDkB/+NDf31+MlZeXw8XFBV27dm21j5+fn95j+vn5iX2aS01NFec/qdVqBAcHm/YEiYiILIxFk3R29S8xd+5c5OXl4cCBA3oxlUql87MgCHptzTXvY6h/a/ezePFiLFiwQPy5qqqKhRMRWYQgCKitrbXa49fU1HT4fbq5ubX5d5s6Xlv/5nxNfmM3RdO8efPw8ccfY//+/QgKChLbAwICANwaKerevbvYfuHCBXH0KSAgAPX19aisrNQZbbpw4QKGDx8u9qmoqNB73IsXL7Y4Cc7V1ZVn8hCRVdTW1mLcuHFWe3xzPPbnn38Od3f3Dr9fal19fb1JcSWx+ctzgiBg7ty52LFjB7766iv07NlTJ96zZ08EBARg7969Ylt9fT327dsnFkSRkZFwdnbW6VNWVoZTp06JfaKjo6HRaHDkyBGxz+HDh6HRaMQ+REREpFw2P9I0Z84cbN68GR999BE8PT3F+UVqtRru7u5QqVRISkrCsmXLEB4ejvDwcCxbtgydOnVCfHy82HfmzJlITk6Gj48PvL29sXDhQgwYMEBcTde3b1/ExsZi1qxZWLduHQDgiSeeQFxcHFfOEZHNcXNzw+eff26W+66srMRf/vKXFuNbt27VmyPaEdzc3Dr8PqltPEtUOpsvmt58800AwMiRI3XaN2zYgBkzZgAAnn32WdTU1GD27NmorKxEVFQU9uzZA09PT7H/6tWr4eTkhMmTJ6OmpgajRo1Ceno6HB0dxT6ZmZmYP3++uMpuwoQJSEtLM+8TJCIygkqlMtulLHd3d3Tu3BnV1dV6sc6dOyMwMNAsj0vWoVKpIAhCq3G6RSW09i9F7VJVVQW1Wg2NRgMvLy9rp2NW99xzT5t99u/fb4FMyBL4eiuTodedr7P8xMXFtbpljpeXFz755BMLZmR5Uj+/bX5OExERWUfzS3Q8IUGe2tpjkHsQ/oZFExERGfToo4/q/Dxr1iwrZUJkG1g0ERFRm8w16ZzInrBoIiIiIpKARRMRERGRBDa/5QAREZHS8dgc28CiiYiIyMbx2BzbwMtzRERERBJwpImIiMjGmfPYHKD1kSRzPa49HpvDoomIiMjGmfPYHODWTu/cAb5tvDxHREREGDx4sM7PUVFRVsrEdrFoIiIiIrzyyis6P69YscJKmdguFk1ERESkgzvAG8aiiYiIiEgCTgQnkglufkdEZF4smohkgpvfERGZFy/PEREREUnAkSYimTDn5nf79u3DsmXLWow///zzGDFiRIc/rj1ufkdE8sWiiUgmzLn5XWxsbKtFU2xsrFkel4jIlvDyHBFJ0tLOwNwxmIiUgiNNRCSZo6MjGhsbxZ+dnPgnxBBrr2TsKLc/Bzk8H67GJFPxLx4RSbZ7926dVXJfffWVFbOxXdZeyWgODz74oLVTMBlXY5KpeHmOiIzCHYOJSGk40kREZEaNDzTa719aAUDT1VhHAPZ4Zesm4PhfR2tnQTJhr29lIiL74AT7/kvrbO0EbB/nsNkmc8xhs+e3MhERkdVxDpttMsccNhZNMmbtbz88i4yIiOSERZOMWfvbD88iIyKleQ6Ai7WTMJIAoOF//+0M+5zCVg/gX2a8fxZNREREHcQFgItdlhu3uFo7AZMJZr13Fk0yZs6zyKSMIpnjse31LDJrXyrtKJwoSkRKxqJJxsx5Ftn+/ftxzz33tBqn31j7Uqk5cKIoESkNN7cko/EsMiIiUhKONBFZ2H/uuQpXR/NedzcXQQDqtbf+28UBsMcrW3WNKszZ38XaaZBM1QMw97waalm9me+fRROZ5PPPP9e57MRRpra5Ogpws+MNiu3/YpaFP9BuWvbhqBkL//ubc+UWWR+LJuowPIuMSB+P8CCSDxZNVsLVVLaJq6mIyBT2vE+THHCfJpniairbxNVU1NHs+sBeOeCBvZLJYXNLc+NbmcjC6hrb7kPmY/F/f3s/sJfahXOa5I1vZRtw/e4EwMFOXwpBALT/m2np4GSfy6m0N+FxItNiDzdnf1eLPRbZAHueCC4AaCoyHWGfQw/2/O9PNsdOP6llRrDj5akqFeDobO0sTGPP//5k83hpSP7MefqCJdXW1orTLD766CO7PYGhiTnyZ9FkAzy+22ztFMiCVsVwnyZrqmtUYUF2F2unQTJiztMXrMXNzU12z6kjsGhq5o033sCKFStQVlaG/v37Y82aNfjDH/5g7bRIRviBLX8cebBN9p4/WR+Lptts27YNSUlJeOONNxATE4N169bh/vvvxw8//ICQkBBrp0dEdsISIw9y2bbkdtzyo2WWeL0tvYWMPb7eKkHghI4mUVFRuPvuu/Hmm2+KbX379sXEiRORmpqq17+urg51dXXiz1VVVQgODoZGo4GXl1erj1VTUyO7LQfkwFxbDljqD54ctn24nblHN+zxj3YTOf4N4ZYfLePrbV5VVVVQq9Vtfn5zpOl/6uvrcfz4cSxatEinfezYsTh48KDB30lNTcWSJUuMejxLDN/zQ7T9zHXfcpzzYAmcV0FEtoQjTf9z/vx59OjRA9nZ2Rg+fLjYvmzZMmzcuBH5+fl6v2PKSJMlWGJ0QxAE8d/A1dXV7N/a7XlkwNz4eisLX29l4ettXhxpMlLzF1AQhBZfVFdXV7i6uloiLaNYanSjU6dOZn8Mahtfb2Xh660sfL1tg4O1E7AVvr6+cHR0RHl5uU77hQsX4O/vb6WsiIiIyFawaPofFxcXREZGYu/evTrte/fu1blcR0RERMrEy3O3WbBgARITEzF48GBER0fj7bffRnFxMZ566ilrp0ZERERWxqLpNlOmTMHly5exdOlSlJWVISIiArt370ZoaKi1UyMiIiIr4+q5DiR19j0RERHZDqmf35zTRERERCQBiyYiIiIiCVg0EREREUnAoomIiIhIAhZNRERERBKwaCIiIiKSgEUTERERkQQsmoiIiIgk4I7gHahpn9CqqiorZ0JERERSNX1ut7XfN4umDnTt2jUAQHBwsJUzISIiova6du0a1Gp1i3Eeo9KBtFotzp8/D09PT6hUKmunYzFVVVUIDg5GSUkJj49RAL7eysLXW1mU+noLgoBr164hMDAQDg4tz1ziSFMHcnBwQFBQkLXTsBovLy9FvcmUjq+3svD1VhYlvt6tjTA14URwIiIiIglYNBERERFJwKKJTObq6oqXX34Zrq6u1k6FLICvt7Lw9VYWvt6t40RwIiIiIgk40kREREQkAYsmIiIiIglYNBERERFJwKKJiIiISAIWTWSSN954Az179oSbmxsiIyPx7bffWjslMpP9+/fjgQceQGBgIFQqFXbt2mXtlMhMUlNTMWTIEHh6esLPzw8TJ05Efn6+tdMiM3nzzTcxcOBAcUPL6OhofPbZZ9ZOyyaxaCKjbdu2DUlJSXjhhRfw3Xff4Q9/+APuv/9+FBcXWzs1MoPr169j0KBBSEtLs3YqZGb79u3DnDlzkJOTg7179+LmzZsYO3Ysrl+/bu3UyAyCgoLw6quv4tixYzh27Bjuu+8+PPjggzh9+rS1U7M53HKAjBYVFYW7774bb775ptjWt29fTJw4EampqVbMjMxNpVJh586dmDhxorVTIQu4ePEi/Pz8sG/fPtxzzz3WTocswNvbGytWrMDMmTOtnYpN4UgTGaW+vh7Hjx/H2LFjddrHjh2LgwcPWikrIjIHjUYD4NYHKclbY2Mjtm7diuvXryM6Otra6dgcHthLRrl06RIaGxvh7++v0+7v74/y8nIrZUVEHU0QBCxYsAC///3vERERYe10yExOnjyJ6Oho1NbWonPnzti5cyf69etn7bRsDosmMolKpdL5WRAEvTYisl9z585FXl4eDhw4YO1UyIz69OmD3NxcXL16Fdu3b8f06dOxb98+Fk7NsGgio/j6+sLR0VFvVOnChQt6o09EZJ/mzZuHjz/+GPv370dQUJC10yEzcnFxQe/evQEAgwcPxtGjR/Hvf/8b69ats3JmtoVzmsgoLi4uiIyMxN69e3Xa9+7di+HDh1spKyLqCIIgYO7cudixYwe++uor9OzZ09opkYUJgoC6ujprp2FzONJERluwYAESExMxePBgREdH4+2330ZxcTGeeuopa6dGZlBdXY2zZ8+KPxcUFCA3Nxfe3t4ICQmxYmbU0ebMmYPNmzfjo48+gqenpziirFar4e7ubuXsqKM9//zzuP/++xEcHIxr165h69at+Oabb5CVlWXt1GwOtxwgk7zxxhtYvnw5ysrKEBERgdWrV3NJskx98803uPfee/Xap0+fjvT0dMsnRGbT0rzEDRs2YMaMGZZNhsxu5syZ+PLLL1FWVga1Wo2BAwfiueeew5gxY6ydms1h0UREREQkAec0EREREUnAoomIiIhIAhZNRERERBKwaCIiIiKSgEUTERERkQQsmoiIiIgkYNFEREREJAGLJiIiIiIJWDQREZmBSqXCrl27rJ0GEXUgFk1EZHNmzJgBlUoFlUoFZ2dn9OrVCwsXLsT169etnZqelJQU3HnnnXrtZWVluP/++y2fEBGZDQ/sJSKbFBsbiw0bNqChoQHffvstHn/8cVy/fh1vvvmmTr+GhgY4OztbKcuWBQQEWDsFIupgHGkiIpvk6uqKgIAABAcHIz4+HgkJCdi1a5c4svP//t//Q69eveDq6gpBEFBcXIwHH3wQnTt3hpeXFyZPnoyKigrx/m7/vZCQEHTu3BlPP/00GhsbsXz5cgQEBMDPzw+vvPKKTh6t3W96ejqWLFmC77//XhwZazq8uPnluZMnT+K+++6Du7s7fHx88MQTT6C6ulqMz5gxAxMnTsRrr72G7t27w8fHB3PmzEFDQ4PY54033kB4eDjc3Nzg7++Phx9+2Az/8kTUEo40EZFdcHd3FwuIs2fP4v3338f27dvh6OgIAJg4cSI8PDywb98+3Lx5E7Nnz8aUKVPwzTffiPfxyy+/4LPPPkNWVhZ++eUXPPzwwygoKMAdd9yBffv24eDBg3jssccwatQoDBs2DIIgtHq/U6ZMwalTp5CVlYUvvvgCAKBWq/Vyv3HjBmJjYzFs2DAcPXoUFy5cwOOPP465c+eKRRYAfP311+jevTu+/vprnD17FlOmTMGdd96JWbNm4dixY5g/fz7ee+89DB8+HFeuXMG3335rvn9wItInEBHZmOnTpwsPPvig+PPhw4cFHx8fYfLkycLLL78sODs7CxcuXBDje/bsERwdHYXi4mKx7fTp0wIA4ciRI4IgCMLLL78sdOrUSaiqqhL7jBs3TggLCxMaGxvFtj59+gipqantut9BgwbpPQcAws6dOwVBEIS3335b6Nq1q1BdXS3GP/30U8HBwUEoLy8Xn3NoaKhw8+ZNsc+kSZOEKVOmCIIgCNu3bxe8vLx08iciy+LlOSKySZ988gk6d+4MNzc3REdH45577sHatWsBAKGhoejWrZvY98yZMwgODkZwcLDY1q9fP3Tp0gVnzpwR28LCwuDp6Sn+7O/vj379+sHBwUGn7cKFC+2637acOXMGgwYNgoeHh9gWExMDrVaL/Px8sa1///7iyBkAdO/eXcxlzJgxCA0NRa9evZCYmIjMzEzcuHFDcg5EZDoWTURkk+69917k5uYiPz8ftbW12LFjB/z8/ABAp/gAAEEQoFKp9O6jeXvzCeNNq/Oat2m12nbdb1ta699Wfk25eHp64sSJE9iyZQu6d++Ol156CYMGDcLVq1cl50FEpmHRREQ2ycPDA71790ZoaGibq+P69euH4uJilJSUiG0//PADNBoN+vbta3QOUu7XxcUFjY2Nbd5Pbm6uzpYJ2dnZcHBwwB133CE5HycnJ4wePRrLly9HXl4eCgsL8dVXX7XzWRGRsVg0EZHdGz16NAYOHIiEhAScOHECR44cwSOPPIIRI0Zg8ODBZr3fsLAwFBQUIDc3F5cuXUJdXZ3e/SQkJMDNzQ3Tp0/HqVOn8PXXX2PevHlITEyEv7+/pFw++eQTvP7668jNzUVRURE2bdoErVaLPn36GP38iKh9WDQRkd1rWt7ftWtX3HPPPRg9ejR69eqFbdu2mf1+H3roIcTGxuLee+9Ft27dsGXLFr376dSpEz7//HNcuXIFQ4YMwcMPP4xRo0YhLS1Nci5dunTBjh07cN9996Fv37546623sGXLFvTv39+k50hE0qkEQRCsnQQRERGRreNIExEREZEELJqIiIiIJGDRRERERCQBiyYiIiIiCVg0EREREUnAoomIiIhIAhZNRERERBKwaCIiIiKSgEUTERERkQQsmoiIiIgkYNFEREREJMH/B5bbFdj9SikhAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"Promotions\", y = \"Orders_Count\", data=combined)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c71c9825-52ee-46c9-8157-9d157d42454d",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Promotions</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>0</th>\n",
       "      <td>267296.0</td>\n",
       "      <td>211.184245</td>\n",
       "      <td>258.580947</td>\n",
       "      <td>13.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>9045.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32589.0</td>\n",
       "      <td>406.138360</td>\n",
       "      <td>540.504742</td>\n",
       "      <td>13.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>10140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14243.0</td>\n",
       "      <td>686.356315</td>\n",
       "      <td>865.584090</td>\n",
       "      <td>13.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>377.0</td>\n",
       "      <td>863.0</td>\n",
       "      <td>11110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7309.0</td>\n",
       "      <td>630.252702</td>\n",
       "      <td>772.219294</td>\n",
       "      <td>13.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>728.0</td>\n",
       "      <td>13150.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count        mean         std   min    25%    50%    75%  \\\n",
       "Promotions                                                                \n",
       "0           267296.0  211.184245  258.580947  13.0   42.0  121.0  283.0   \n",
       "1            32589.0  406.138360  540.504742  13.0   94.0  216.0  501.0   \n",
       "2            14243.0  686.356315  865.584090  13.0  175.0  377.0  863.0   \n",
       "3             7309.0  630.252702  772.219294  13.0  230.0  405.0  728.0   \n",
       "\n",
       "                max  \n",
       "Promotions           \n",
       "0            9045.0  \n",
       "1           10140.0  \n",
       "2           11110.0  \n",
       "3           13150.0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.groupby(\"Promotions\")[\"Orders_Count\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "80e78713-ec04-45d0-9f15-597678c49c7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined.drop(['Custom_Promoted', 'Promoted', 'Search_Promotions'], \n",
    "              axis =1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f873150c-70cb-4822-8119-6c558ccfe35b",
   "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>Period_No</th>\n",
       "      <th>Facility_Category</th>\n",
       "      <th>Billing_Amount</th>\n",
       "      <th>Labelled_Price</th>\n",
       "      <th>Orders_Count</th>\n",
       "      <th>Course</th>\n",
       "      <th>Flavour_Profile</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>OC_FW</th>\n",
       "      <th>AOC_FW</th>\n",
       "      <th>MOC_FW</th>\n",
       "      <th>MinOC_FW</th>\n",
       "      <th>MaxAOC_FW</th>\n",
       "      <th>SDAOC_FW</th>\n",
       "      <th>Magic1</th>\n",
       "      <th>Magic2</th>\n",
       "      <th>Magic3</th>\n",
       "      <th>Magic4</th>\n",
       "      <th>Magic5</th>\n",
       "      <th>Magic6</th>\n",
       "      <th>Magic7</th>\n",
       "      <th>Magic8</th>\n",
       "      <th>Magic9</th>\n",
       "      <th>Magic10</th>\n",
       "      <th>Magic11</th>\n",
       "      <th>Magic12</th>\n",
       "      <th>area</th>\n",
       "      <th>Promotions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>c1</td>\n",
       "      <td>192.955562</td>\n",
       "      <td>267.971749</td>\n",
       "      <td>123.0</td>\n",
       "      <td>Finger Foods</td>\n",
       "      <td>Asian Fusion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4118</td>\n",
       "      <td>207.096163</td>\n",
       "      <td>134.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3374.0</td>\n",
       "      <td>258.871277</td>\n",
       "      <td>66360</td>\n",
       "      <td>215.997107</td>\n",
       "      <td>134.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9532.0</td>\n",
       "      <td>289.687066</td>\n",
       "      <td>18103</td>\n",
       "      <td>313.646523</td>\n",
       "      <td>148.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10544.0</td>\n",
       "      <td>473.337118</td>\n",
       "      <td>Others</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>c3</td>\n",
       "      <td>193.384864</td>\n",
       "      <td>203.836706</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Broths &amp; Stews</td>\n",
       "      <td>Asian Fusion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4298</td>\n",
       "      <td>257.586087</td>\n",
       "      <td>109.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7088.0</td>\n",
       "      <td>430.839680</td>\n",
       "      <td>134451</td>\n",
       "      <td>318.114131</td>\n",
       "      <td>161.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13150.0</td>\n",
       "      <td>464.342597</td>\n",
       "      <td>36987</td>\n",
       "      <td>222.396247</td>\n",
       "      <td>122.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9532.0</td>\n",
       "      <td>329.431315</td>\n",
       "      <td>top5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>c3</td>\n",
       "      <td>384.988183</td>\n",
       "      <td>388.957411</td>\n",
       "      <td>204.0</td>\n",
       "      <td>Smoothies &amp; Juices</td>\n",
       "      <td>Western</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3635</td>\n",
       "      <td>160.554058</td>\n",
       "      <td>95.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2930.0</td>\n",
       "      <td>216.484447</td>\n",
       "      <td>134451</td>\n",
       "      <td>318.114131</td>\n",
       "      <td>161.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13150.0</td>\n",
       "      <td>464.342597</td>\n",
       "      <td>6653</td>\n",
       "      <td>160.297760</td>\n",
       "      <td>94.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4009.0</td>\n",
       "      <td>224.186851</td>\n",
       "      <td>Others</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>c3</td>\n",
       "      <td>255.251864</td>\n",
       "      <td>263.923874</td>\n",
       "      <td>257.0</td>\n",
       "      <td>Grain Bowls</td>\n",
       "      <td>South Asian</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3261</td>\n",
       "      <td>200.477461</td>\n",
       "      <td>108.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4726.0</td>\n",
       "      <td>316.956115</td>\n",
       "      <td>81986</td>\n",
       "      <td>203.351487</td>\n",
       "      <td>122.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6426.0</td>\n",
       "      <td>263.878412</td>\n",
       "      <td>7575</td>\n",
       "      <td>244.712739</td>\n",
       "      <td>149.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4726.0</td>\n",
       "      <td>298.571900</td>\n",
       "      <td>Others</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>c3</td>\n",
       "      <td>319.949514</td>\n",
       "      <td>326.128813</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Appetizers</td>\n",
       "      <td>Asian Fusion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3240</td>\n",
       "      <td>168.157716</td>\n",
       "      <td>95.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2741.0</td>\n",
       "      <td>233.959012</td>\n",
       "      <td>81986</td>\n",
       "      <td>203.351487</td>\n",
       "      <td>122.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6426.0</td>\n",
       "      <td>263.878412</td>\n",
       "      <td>17767</td>\n",
       "      <td>184.067372</td>\n",
       "      <td>107.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5334.0</td>\n",
       "      <td>252.308016</td>\n",
       "      <td>Others</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Period_No Facility_Category  Billing_Amount  Labelled_Price  Orders_Count  \\\n",
       "0          1                c1      192.955562      267.971749         123.0   \n",
       "1          1                c3      193.384864      203.836706          55.0   \n",
       "2          1                c3      384.988183      388.957411         204.0   \n",
       "3          1                c3      255.251864      263.923874         257.0   \n",
       "4          1                c3      319.949514      326.128813          14.0   \n",
       "\n",
       "               Course Flavour_Profile  Quantity  OC_FW      AOC_FW  MOC_FW  \\\n",
       "0        Finger Foods    Asian Fusion       1.0   4118  207.096163   134.0   \n",
       "1      Broths & Stews    Asian Fusion       1.0   4298  257.586087   109.0   \n",
       "2  Smoothies & Juices         Western       1.0   3635  160.554058    95.0   \n",
       "3         Grain Bowls     South Asian       1.0   3261  200.477461   108.0   \n",
       "4          Appetizers    Asian Fusion       1.0   3240  168.157716    95.0   \n",
       "\n",
       "   MinOC_FW  MaxAOC_FW    SDAOC_FW  Magic1      Magic2  Magic3  Magic4  \\\n",
       "0      13.0     3374.0  258.871277   66360  215.997107   134.0    13.0   \n",
       "1      13.0     7088.0  430.839680  134451  318.114131   161.0    13.0   \n",
       "2      13.0     2930.0  216.484447  134451  318.114131   161.0    13.0   \n",
       "3      13.0     4726.0  316.956115   81986  203.351487   122.0    13.0   \n",
       "4      13.0     2741.0  233.959012   81986  203.351487   122.0    13.0   \n",
       "\n",
       "    Magic5      Magic6  Magic7      Magic8  Magic9  Magic10  Magic11  \\\n",
       "0   9532.0  289.687066   18103  313.646523   148.0     13.0  10544.0   \n",
       "1  13150.0  464.342597   36987  222.396247   122.0     13.0   9532.0   \n",
       "2  13150.0  464.342597    6653  160.297760    94.0     13.0   4009.0   \n",
       "3   6426.0  263.878412    7575  244.712739   149.0     13.0   4726.0   \n",
       "4   6426.0  263.878412   17767  184.067372   107.0     13.0   5334.0   \n",
       "\n",
       "      Magic12    area  Promotions  \n",
       "0  473.337118  Others           0  \n",
       "1  329.431315    top5           0  \n",
       "2  224.186851  Others           0  \n",
       "3  298.571900  Others           0  \n",
       "4  252.308016  Others           0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "bfb942c4-2249-4cbc-b1d5-a58694c53aa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Finger Foods', 'Broths & Stews', 'Smoothies & Juices',\n",
       "       'Grain Bowls', 'Appetizers', 'Grilled Vegetables',\n",
       "       'Sweets & Treats', 'Wraps & Rolls', 'Noodles & Veggies',\n",
       "       'Fresh Greens', 'Condiments & Dips', 'Plant-Based Proteins',\n",
       "       'Stir-Fried Rice', 'Flatbreads'], dtype=object)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.Course.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b93146be-8af1-4ada-ac29-69ffab774ac5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Asian Fusion', 'Western', 'South Asian', 'Mediterranean'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.Flavour_Profile.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "dbd2d984-8f1e-4336-941c-94f6ee87022b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"Flavour_Profile\", y = \"Orders_Count\", data=combined)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "4a27ac8e-c44e-4e78-80c7-179950622b41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"Course\", y = \"Orders_Count\", data=combined)\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7093fbac-95f1-45f5-8364-a46398f11101",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Course</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>Appetizers</th>\n",
       "      <td>20382.0</td>\n",
       "      <td>155.968992</td>\n",
       "      <td>143.935574</td>\n",
       "      <td>13.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>1554.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Broths &amp; Stews</th>\n",
       "      <td>8889.0</td>\n",
       "      <td>88.900551</td>\n",
       "      <td>72.350096</td>\n",
       "      <td>13.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>689.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condiments &amp; Dips</th>\n",
       "      <td>9350.0</td>\n",
       "      <td>281.830909</td>\n",
       "      <td>222.736124</td>\n",
       "      <td>13.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>1620.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finger Foods</th>\n",
       "      <td>20895.0</td>\n",
       "      <td>164.194783</td>\n",
       "      <td>164.063729</td>\n",
       "      <td>13.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>2404.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flatbreads</th>\n",
       "      <td>22779.0</td>\n",
       "      <td>233.691382</td>\n",
       "      <td>235.383834</td>\n",
       "      <td>13.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>2499.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fresh Greens</th>\n",
       "      <td>22850.0</td>\n",
       "      <td>389.665996</td>\n",
       "      <td>372.423667</td>\n",
       "      <td>13.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>459.0</td>\n",
       "      <td>8059.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grain Bowls</th>\n",
       "      <td>22937.0</td>\n",
       "      <td>600.332825</td>\n",
       "      <td>614.775419</td>\n",
       "      <td>13.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>729.0</td>\n",
       "      <td>10544.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grilled Vegetables</th>\n",
       "      <td>10121.0</td>\n",
       "      <td>85.583638</td>\n",
       "      <td>80.312274</td>\n",
       "      <td>13.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>879.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Noodles &amp; Veggies</th>\n",
       "      <td>18601.0</td>\n",
       "      <td>58.128111</td>\n",
       "      <td>53.681616</td>\n",
       "      <td>13.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>690.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Plant-Based Proteins</th>\n",
       "      <td>18888.0</td>\n",
       "      <td>98.913596</td>\n",
       "      <td>137.601044</td>\n",
       "      <td>13.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>1620.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Smoothies &amp; Juices</th>\n",
       "      <td>88363.0</td>\n",
       "      <td>314.610233</td>\n",
       "      <td>381.076319</td>\n",
       "      <td>13.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>9303.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stir-Fried Rice</th>\n",
       "      <td>14143.0</td>\n",
       "      <td>30.439086</td>\n",
       "      <td>21.804170</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>202.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweets &amp; Treats</th>\n",
       "      <td>20344.0</td>\n",
       "      <td>67.889894</td>\n",
       "      <td>58.899418</td>\n",
       "      <td>13.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>744.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wraps &amp; Rolls</th>\n",
       "      <td>22895.0</td>\n",
       "      <td>550.654903</td>\n",
       "      <td>673.390923</td>\n",
       "      <td>13.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>607.0</td>\n",
       "      <td>13150.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count        mean         std   min    25%    50%  \\\n",
       "Course                                                                      \n",
       "Appetizers            20382.0  155.968992  143.935574  13.0   54.0  109.0   \n",
       "Broths & Stews         8889.0   88.900551   72.350096  13.0   40.0   68.0   \n",
       "Condiments & Dips      9350.0  281.830909  222.736124  13.0  122.0  216.0   \n",
       "Finger Foods          20895.0  164.194783  164.063729  13.0   54.0  121.0   \n",
       "Flatbreads            22779.0  233.691382  235.383834  13.0   82.0  161.0   \n",
       "Fresh Greens          22850.0  389.665996  372.423667  13.0  190.0  298.0   \n",
       "Grain Bowls           22937.0  600.332825  614.775419  13.0  257.0  433.0   \n",
       "Grilled Vegetables    10121.0   85.583638   80.312274  13.0   28.0   67.0   \n",
       "Noodles & Veggies     18601.0   58.128111   53.681616  13.0   26.0   41.0   \n",
       "Plant-Based Proteins  18888.0   98.913596  137.601044  13.0   27.0   54.0   \n",
       "Smoothies & Juices    88363.0  314.610233  381.076319  13.0   80.0  188.0   \n",
       "Stir-Fried Rice       14143.0   30.439086   21.804170  13.0   14.0   26.0   \n",
       "Sweets & Treats       20344.0   67.889894   58.899418  13.0   27.0   53.0   \n",
       "Wraps & Rolls         22895.0  550.654903  673.390923  13.0  216.0  352.0   \n",
       "\n",
       "                        75%      max  \n",
       "Course                                \n",
       "Appetizers            215.0   1554.0  \n",
       "Broths & Stews        121.0    689.0  \n",
       "Condiments & Dips     391.0   1620.0  \n",
       "Finger Foods          216.0   2404.0  \n",
       "Flatbreads            296.0   2499.0  \n",
       "Fresh Greens          459.0   8059.0  \n",
       "Grain Bowls           729.0  10544.0  \n",
       "Grilled Vegetables    109.0    879.0  \n",
       "Noodles & Veggies      69.0    690.0  \n",
       "Plant-Based Proteins  107.0   1620.0  \n",
       "Smoothies & Juices    393.0   9303.0  \n",
       "Stir-Fried Rice        41.0    202.0  \n",
       "Sweets & Treats        82.0    744.0  \n",
       "Wraps & Rolls         607.0  13150.0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.groupby(\"Course\")[\"Orders_Count\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "099aee69",
   "metadata": {},
   "outputs": [],
   "source": [
    "#combined.groupby(\"Operational_Region_Coverage_Area\")[\"Orders_Count\"].describe()\n",
    "\n",
    "combined[\"Magic13\"] = combined.groupby(\"Course\")[\"Orders_Count\"].transform('count')\n",
    "combined[\"Magic14\"] = combined.groupby(\"Course\")[\"Orders_Count\"].transform('mean')\n",
    "combined[\"Magic15\"] = combined.groupby(\"Course\")[\"Orders_Count\"].transform('median')\n",
    "combined[\"Magic16\"] = combined.groupby(\"Course\")[\"Orders_Count\"].transform('min')\n",
    "combined[\"Magic17\"] = combined.groupby(\"Course\")[\"Orders_Count\"].transform('max')\n",
    "combined[\"Magic18\"] = combined.groupby(\"Course\")[\"Orders_Count\"].transform('std')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e4126b21-f4fc-4693-9207-50c805b6fa25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Smoothies & Juices', 'Grain Bowls', 'Wraps & Rolls', 'Flatbreads',\n",
      "       'Appetizers'],\n",
      "      dtype='object', name='Course')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(combined.Course.value_counts()[:5].index)\n",
    "combined.Course.value_counts()[:5].plot(kind = \"bar\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "66e0cbca-e949-4309-a94c-a75b3e70903a",
   "metadata": {},
   "outputs": [],
   "source": [
    "foods = ['Smoothies & Juices', 'Grain Bowls', 'Wraps & Rolls', 'Flatbreads',\n",
    "       'Fresh Greens']\n",
    "\n",
    "def khana_khazana(x):\n",
    "    if x in foods:\n",
    "        return(\"Top5_Foods\")\n",
    "    else:\n",
    "        return(\"Other_Foods\")\n",
    "\n",
    "combined[\"Course_Cat\"]= combined.Course.apply(khana_khazana)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7b4a0f81-9ae0-48b9-b43a-ac7b3d722273",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"Course_Cat\", y = \"Orders_Count\", data=combined)\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d68a6b58-42c5-4cb0-a566-f3c4bb0f0cd8",
   "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>Period_No</th>\n",
       "      <th>Facility_Category</th>\n",
       "      <th>Billing_Amount</th>\n",
       "      <th>Labelled_Price</th>\n",
       "      <th>Orders_Count</th>\n",
       "      <th>Course</th>\n",
       "      <th>Flavour_Profile</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>OC_FW</th>\n",
       "      <th>AOC_FW</th>\n",
       "      <th>MOC_FW</th>\n",
       "      <th>MinOC_FW</th>\n",
       "      <th>MaxAOC_FW</th>\n",
       "      <th>SDAOC_FW</th>\n",
       "      <th>Magic1</th>\n",
       "      <th>Magic2</th>\n",
       "      <th>Magic3</th>\n",
       "      <th>Magic4</th>\n",
       "      <th>Magic5</th>\n",
       "      <th>Magic6</th>\n",
       "      <th>Magic7</th>\n",
       "      <th>Magic8</th>\n",
       "      <th>Magic9</th>\n",
       "      <th>Magic10</th>\n",
       "      <th>Magic11</th>\n",
       "      <th>Magic12</th>\n",
       "      <th>area</th>\n",
       "      <th>Promotions</th>\n",
       "      <th>Course_Cat</th>\n",
       "      <th>Magic13</th>\n",
       "      <th>Magic14</th>\n",
       "      <th>Magic15</th>\n",
       "      <th>Magic16</th>\n",
       "      <th>Magic17</th>\n",
       "      <th>Magic18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>c1</td>\n",
       "      <td>192.955562</td>\n",
       "      <td>267.971749</td>\n",
       "      <td>123.0</td>\n",
       "      <td>Finger Foods</td>\n",
       "      <td>Asian Fusion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4118</td>\n",
       "      <td>207.096163</td>\n",
       "      <td>134.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3374.0</td>\n",
       "      <td>258.871277</td>\n",
       "      <td>66360</td>\n",
       "      <td>215.997107</td>\n",
       "      <td>134.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9532.0</td>\n",
       "      <td>289.687066</td>\n",
       "      <td>18103</td>\n",
       "      <td>313.646523</td>\n",
       "      <td>148.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10544.0</td>\n",
       "      <td>473.337118</td>\n",
       "      <td>Others</td>\n",
       "      <td>0</td>\n",
       "      <td>Other_Foods</td>\n",
       "      <td>20895</td>\n",
       "      <td>164.194783</td>\n",
       "      <td>121.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2404.0</td>\n",
       "      <td>164.063729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>c3</td>\n",
       "      <td>193.384864</td>\n",
       "      <td>203.836706</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Broths &amp; Stews</td>\n",
       "      <td>Asian Fusion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4298</td>\n",
       "      <td>257.586087</td>\n",
       "      <td>109.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7088.0</td>\n",
       "      <td>430.839680</td>\n",
       "      <td>134451</td>\n",
       "      <td>318.114131</td>\n",
       "      <td>161.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13150.0</td>\n",
       "      <td>464.342597</td>\n",
       "      <td>36987</td>\n",
       "      <td>222.396247</td>\n",
       "      <td>122.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9532.0</td>\n",
       "      <td>329.431315</td>\n",
       "      <td>top5</td>\n",
       "      <td>0</td>\n",
       "      <td>Other_Foods</td>\n",
       "      <td>8889</td>\n",
       "      <td>88.900551</td>\n",
       "      <td>68.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>72.350096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>c3</td>\n",
       "      <td>384.988183</td>\n",
       "      <td>388.957411</td>\n",
       "      <td>204.0</td>\n",
       "      <td>Smoothies &amp; Juices</td>\n",
       "      <td>Western</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3635</td>\n",
       "      <td>160.554058</td>\n",
       "      <td>95.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2930.0</td>\n",
       "      <td>216.484447</td>\n",
       "      <td>134451</td>\n",
       "      <td>318.114131</td>\n",
       "      <td>161.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13150.0</td>\n",
       "      <td>464.342597</td>\n",
       "      <td>6653</td>\n",
       "      <td>160.297760</td>\n",
       "      <td>94.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4009.0</td>\n",
       "      <td>224.186851</td>\n",
       "      <td>Others</td>\n",
       "      <td>0</td>\n",
       "      <td>Top5_Foods</td>\n",
       "      <td>88363</td>\n",
       "      <td>314.610233</td>\n",
       "      <td>188.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9303.0</td>\n",
       "      <td>381.076319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>c3</td>\n",
       "      <td>255.251864</td>\n",
       "      <td>263.923874</td>\n",
       "      <td>257.0</td>\n",
       "      <td>Grain Bowls</td>\n",
       "      <td>South Asian</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3261</td>\n",
       "      <td>200.477461</td>\n",
       "      <td>108.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4726.0</td>\n",
       "      <td>316.956115</td>\n",
       "      <td>81986</td>\n",
       "      <td>203.351487</td>\n",
       "      <td>122.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6426.0</td>\n",
       "      <td>263.878412</td>\n",
       "      <td>7575</td>\n",
       "      <td>244.712739</td>\n",
       "      <td>149.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4726.0</td>\n",
       "      <td>298.571900</td>\n",
       "      <td>Others</td>\n",
       "      <td>0</td>\n",
       "      <td>Top5_Foods</td>\n",
       "      <td>22937</td>\n",
       "      <td>600.332825</td>\n",
       "      <td>433.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10544.0</td>\n",
       "      <td>614.775419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>c3</td>\n",
       "      <td>319.949514</td>\n",
       "      <td>326.128813</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Appetizers</td>\n",
       "      <td>Asian Fusion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3240</td>\n",
       "      <td>168.157716</td>\n",
       "      <td>95.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2741.0</td>\n",
       "      <td>233.959012</td>\n",
       "      <td>81986</td>\n",
       "      <td>203.351487</td>\n",
       "      <td>122.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6426.0</td>\n",
       "      <td>263.878412</td>\n",
       "      <td>17767</td>\n",
       "      <td>184.067372</td>\n",
       "      <td>107.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5334.0</td>\n",
       "      <td>252.308016</td>\n",
       "      <td>Others</td>\n",
       "      <td>0</td>\n",
       "      <td>Other_Foods</td>\n",
       "      <td>20382</td>\n",
       "      <td>155.968992</td>\n",
       "      <td>109.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1554.0</td>\n",
       "      <td>143.935574</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Period_No Facility_Category  Billing_Amount  Labelled_Price  Orders_Count  \\\n",
       "0          1                c1      192.955562      267.971749         123.0   \n",
       "1          1                c3      193.384864      203.836706          55.0   \n",
       "2          1                c3      384.988183      388.957411         204.0   \n",
       "3          1                c3      255.251864      263.923874         257.0   \n",
       "4          1                c3      319.949514      326.128813          14.0   \n",
       "\n",
       "               Course Flavour_Profile  Quantity  OC_FW      AOC_FW  MOC_FW  \\\n",
       "0        Finger Foods    Asian Fusion       1.0   4118  207.096163   134.0   \n",
       "1      Broths & Stews    Asian Fusion       1.0   4298  257.586087   109.0   \n",
       "2  Smoothies & Juices         Western       1.0   3635  160.554058    95.0   \n",
       "3         Grain Bowls     South Asian       1.0   3261  200.477461   108.0   \n",
       "4          Appetizers    Asian Fusion       1.0   3240  168.157716    95.0   \n",
       "\n",
       "   MinOC_FW  MaxAOC_FW    SDAOC_FW  Magic1      Magic2  Magic3  Magic4  \\\n",
       "0      13.0     3374.0  258.871277   66360  215.997107   134.0    13.0   \n",
       "1      13.0     7088.0  430.839680  134451  318.114131   161.0    13.0   \n",
       "2      13.0     2930.0  216.484447  134451  318.114131   161.0    13.0   \n",
       "3      13.0     4726.0  316.956115   81986  203.351487   122.0    13.0   \n",
       "4      13.0     2741.0  233.959012   81986  203.351487   122.0    13.0   \n",
       "\n",
       "    Magic5      Magic6  Magic7      Magic8  Magic9  Magic10  Magic11  \\\n",
       "0   9532.0  289.687066   18103  313.646523   148.0     13.0  10544.0   \n",
       "1  13150.0  464.342597   36987  222.396247   122.0     13.0   9532.0   \n",
       "2  13150.0  464.342597    6653  160.297760    94.0     13.0   4009.0   \n",
       "3   6426.0  263.878412    7575  244.712739   149.0     13.0   4726.0   \n",
       "4   6426.0  263.878412   17767  184.067372   107.0     13.0   5334.0   \n",
       "\n",
       "      Magic12    area  Promotions   Course_Cat  Magic13     Magic14  Magic15  \\\n",
       "0  473.337118  Others           0  Other_Foods    20895  164.194783    121.0   \n",
       "1  329.431315    top5           0  Other_Foods     8889   88.900551     68.0   \n",
       "2  224.186851  Others           0   Top5_Foods    88363  314.610233    188.0   \n",
       "3  298.571900  Others           0   Top5_Foods    22937  600.332825    433.0   \n",
       "4  252.308016  Others           0  Other_Foods    20382  155.968992    109.0   \n",
       "\n",
       "   Magic16  Magic17     Magic18  \n",
       "0     13.0   2404.0  164.063729  \n",
       "1     13.0    689.0   72.350096  \n",
       "2     13.0   9303.0  381.076319  \n",
       "3     13.0  10544.0  614.775419  \n",
       "4     13.0   1554.0  143.935574  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "f5ca03e4-1edf-496d-94b0-65b5b48d9073",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x = 'Period_No', y = 'Orders_Count', data = combined)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "af3cc65d-c740-45d6-95ce-21ba1cef3a95",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Period_No</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>1</th>\n",
       "      <td>3250.0</td>\n",
       "      <td>205.896000</td>\n",
       "      <td>253.210473</td>\n",
       "      <td>13.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>2822.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3290.0</td>\n",
       "      <td>242.832219</td>\n",
       "      <td>289.594577</td>\n",
       "      <td>13.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>2715.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3286.0</td>\n",
       "      <td>229.356665</td>\n",
       "      <td>277.997060</td>\n",
       "      <td>13.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>3172.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3212.0</td>\n",
       "      <td>228.412827</td>\n",
       "      <td>277.741486</td>\n",
       "      <td>13.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>2848.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3240.0</td>\n",
       "      <td>237.643210</td>\n",
       "      <td>294.750044</td>\n",
       "      <td>13.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>323.0</td>\n",
       "      <td>3012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>3097.0</td>\n",
       "      <td>286.625767</td>\n",
       "      <td>372.542244</td>\n",
       "      <td>13.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>378.0</td>\n",
       "      <td>5576.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>3109.0</td>\n",
       "      <td>299.257639</td>\n",
       "      <td>377.704739</td>\n",
       "      <td>13.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>6576.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>3107.0</td>\n",
       "      <td>416.708722</td>\n",
       "      <td>768.388798</td>\n",
       "      <td>13.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>9532.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>3041.0</td>\n",
       "      <td>264.857941</td>\n",
       "      <td>335.030524</td>\n",
       "      <td>13.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>3753.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>3043.0</td>\n",
       "      <td>280.674006</td>\n",
       "      <td>399.364206</td>\n",
       "      <td>13.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>9303.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            count        mean         std   min   25%    50%    75%     max\n",
       "Period_No                                                                  \n",
       "1          3250.0  205.896000  253.210473  13.0  53.0  122.0  269.0  2822.0\n",
       "2          3290.0  242.832219  289.594577  13.0  55.0  148.0  312.0  2715.0\n",
       "3          3286.0  229.356665  277.997060  13.0  54.0  135.0  296.0  3172.0\n",
       "4          3212.0  228.412827  277.741486  13.0  53.0  134.0  310.0  2848.0\n",
       "5          3240.0  237.643210  294.750044  13.0  53.0  123.0  323.0  3012.0\n",
       "...           ...         ...         ...   ...   ...    ...    ...     ...\n",
       "96         3097.0  286.625767  372.542244  13.0  55.0  162.0  378.0  5576.0\n",
       "97         3109.0  299.257639  377.704739  13.0  55.0  163.0  393.0  6576.0\n",
       "98         3107.0  416.708722  768.388798  13.0  67.0  161.0  405.0  9532.0\n",
       "99         3041.0  264.857941  335.030524  13.0  53.0  148.0  352.0  3753.0\n",
       "100        3043.0  280.674006  399.364206  13.0  54.0  149.0  352.0  9303.0\n",
       "\n",
       "[100 rows x 8 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby(\"Period_No\")[\"Orders_Count\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c5982566",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assuming the periods repeat every 12 months (as an example)\n",
    "\n",
    "# Identifying Cyclical Pattern\n",
    "max_period = 12\n",
    "\n",
    "combined['sin_period'] = np.sin(2 * np.pi * combined['Period_No'] / max_period)\n",
    "combined['cos_period'] = np.cos(2 * np.pi * combined['Period_No'] / max_period)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "7813e5ea-16ea-49fc-85c4-4e761797caee",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined.drop([\"Period_No\", \"Course\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "dc4c76ac-6300-4d23-b32a-df0a58095c05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         df        sum_sq       mean_sq             F  \\\n",
      "Facility_Category       2.0  3.853415e+08  1.926708e+08   1845.979470   \n",
      "Flavour_Profile         3.0  1.870291e+09  6.234304e+08   5973.090193   \n",
      "area                    1.0  4.734250e+07  4.734250e+07    453.588769   \n",
      "Course_Cat              1.0  6.080935e+09  6.080935e+09  58261.464850   \n",
      "Promotions              1.0  5.412289e+09  5.412289e+09  51855.170561   \n",
      "Residual           321428.0  3.354846e+10  1.043732e+05           NaN   \n",
      "\n",
      "                          PR(>F)  \n",
      "Facility_Category   0.000000e+00  \n",
      "Flavour_Profile     0.000000e+00  \n",
      "area               1.402311e-100  \n",
      "Course_Cat          0.000000e+00  \n",
      "Promotions          0.000000e+00  \n",
      "Residual                     NaN  \n"
     ]
    }
   ],
   "source": [
    "#cat_cols=['Facility_Category', \"Flavour_Profile\", \"area\", \"Course_Cat\", \"Promotions\"]\n",
    "\n",
    "import statsmodels.formula.api as sfa\n",
    "\n",
    "from statsmodels.stats.anova import anova_lm\n",
    "\n",
    "model = sfa.ols(\"Orders_Count~Facility_Category+Flavour_Profile+area+Course_Cat+Promotions\",\n",
    "        data = combined).fit()\n",
    "\n",
    "print(anova_lm(model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "8639f81f-6d2b-4039-a924-ff3e1b2133bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((321437, 13), (118070, 12), (321437, 35), (118070, 35))"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split the Data into train and test\n",
    "\n",
    "newtrain = combined.loc[0:train.shape[0]-1, :]\n",
    "newtest= combined.loc[train.shape[0]:, :]\n",
    "\n",
    "train.shape, test.shape, newtrain.shape, newtest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "0d02e514",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Billing_Amount', 'Labelled_Price', 'Orders_Count', 'Quantity', 'OC_FW',\n",
       "       'AOC_FW', 'MOC_FW', 'MinOC_FW', 'MaxAOC_FW', 'SDAOC_FW', 'Magic1',\n",
       "       'Magic2', 'Magic3', 'Magic4', 'Magic5', 'Magic6', 'Magic7', 'Magic8',\n",
       "       'Magic9', 'Magic10', 'Magic11', 'Magic12', 'Promotions', 'Magic13',\n",
       "       'Magic14', 'Magic15', 'Magic16', 'Magic17', 'Magic18', 'sin_period',\n",
       "       'cos_period'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newtrain.select_dtypes(include = np.number).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "5ffac38e-3265-487f-8164-5e38aa41be97",
   "metadata": {},
   "outputs": [],
   "source": [
    "nums = ['Billing_Amount', 'Labelled_Price', 'Quantity', 'OC_FW',\n",
    "       'AOC_FW', 'MOC_FW', 'MinOC_FW', 'MaxAOC_FW', 'SDAOC_FW', 'Magic1',\n",
    "       'Magic2', 'Magic3', 'Magic4', 'Magic5', 'Magic6', 'Magic7', 'Magic8',\n",
    "       'Magic9', 'Magic10', 'Magic11', 'Magic12', 'Promotions', 'Magic13',\n",
    "       'Magic14', 'Magic15', 'Magic16', 'Magic17', 'Magic18', 'sin_period',\n",
    "       'cos_period']\n",
    "\n",
    "pvalue = []\n",
    "\n",
    "for i in nums:\n",
    "    test, pvals = stats.ttest_ind(newtrain.loc[:, i], newtrain.Orders_Count)\n",
    "    pvalue.append(pvals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "39e8cc0f-964c-4cd8-b4ae-9123fe4afa2d",
   "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>PValue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Billing_Amount</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Labelled_Price</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sin_period</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Magic18</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Magic17</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Magic16</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Magic15</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Magic14</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Magic13</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Promotions</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Magic12</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Magic11</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Magic10</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Magic9</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Magic8</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Magic7</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Magic6</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Magic5</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Magic4</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Magic3</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Magic2</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Magic1</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SDAOC_FW</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxAOC_FW</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MinOC_FW</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOC_FW</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AOC_FW</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OC_FW</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantity</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cos_period</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                PValue\n",
       "Billing_Amount     0.0\n",
       "Labelled_Price     0.0\n",
       "sin_period         0.0\n",
       "Magic18            0.0\n",
       "Magic17            0.0\n",
       "Magic16            0.0\n",
       "Magic15            0.0\n",
       "Magic14            0.0\n",
       "Magic13            0.0\n",
       "Promotions         0.0\n",
       "Magic12            0.0\n",
       "Magic11            0.0\n",
       "Magic10            0.0\n",
       "Magic9             0.0\n",
       "Magic8             0.0\n",
       "Magic7             0.0\n",
       "Magic6             0.0\n",
       "Magic5             0.0\n",
       "Magic4             0.0\n",
       "Magic3             0.0\n",
       "Magic2             0.0\n",
       "Magic1             0.0\n",
       "SDAOC_FW           0.0\n",
       "MaxAOC_FW          0.0\n",
       "MinOC_FW           0.0\n",
       "MOC_FW             0.0\n",
       "AOC_FW             0.0\n",
       "OC_FW              0.0\n",
       "Quantity           0.0\n",
       "cos_period         0.0"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(pvals, columns = [\"PValue\"], \n",
    "             index = nums).sort_values(by=\"PValue\", ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be28e145-0999-4fc7-ad55-323ce67fffeb",
   "metadata": {},
   "source": [
    "## Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "24988d12-1c11-4782-80b1-e016e01cb1f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "newtest.drop(\"Orders_Count\", axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "f8b9d939-2eda-4107-b0e5-77f9a07ac501",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "X = newtrain.drop('Orders_Count', axis =1)\n",
    "y = newtrain.Orders_Count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "671fad82-546e-4e0c-95d8-9e8f90418f65",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestRegressor()\n",
    "\n",
    "dummytrain = pd.get_dummies(X,drop_first = True)\n",
    "dummytest = pd.get_dummies(newtest,drop_first = True)\n",
    "\n",
    "pred_rf = rf.fit(dummytrain, y).predict(dummytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "b26c37f1-5004-40da-b1f8-e4e44489f90c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "gbm = GradientBoostingRegressor()\n",
    "\n",
    "pred_gbm = gbm.fit(dummytrain, y).predict(dummytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "e0270d0f-23dc-4c8d-8ec1-d23f72c595cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBRegressor\n",
    "\n",
    "xgb = XGBRegressor()\n",
    "\n",
    "pred_xgb = xgb.fit(dummytrain, y).predict(dummytest)\n",
    "submission3 = pd.DataFrame({\"Orders_Count\":pred_xgb})\n",
    "submission3.to_csv(\"XGBModel.csv\", index = False) # 218"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "20863743-0666-42d2-a44a-9d9b58c1b34a",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\"Orders_Count\":pred_rf})\n",
    "submission.to_csv(\"RFModel.csv\", index = False)\n",
    "\n",
    "submission2 = pd.DataFrame({\"Orders_Count\":pred_gbm})\n",
    "submission2.to_csv(\"GBMModel.csv\", index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57b1f6dc",
   "metadata": {},
   "source": [
    "### Neural Net Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "f3c3f869",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1ms/step - loss: 824270.5625 - mae: 364.2554\n",
      "Epoch 2/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1ms/step - loss: 109670.1875 - mae: 175.8866\n",
      "Epoch 3/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 1ms/step - loss: 98219.2891 - mae: 163.1335\n",
      "Epoch 4/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1ms/step - loss: 96486.0234 - mae: 162.1399\n",
      "Epoch 5/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1ms/step - loss: 91237.6953 - mae: 157.5733\n",
      "Epoch 6/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 1ms/step - loss: 86223.5703 - mae: 154.8953\n",
      "Epoch 7/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 1ms/step - loss: 88142.5547 - mae: 156.7052\n",
      "Epoch 8/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 1ms/step - loss: 85716.1953 - mae: 155.6472\n",
      "Epoch 9/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 1ms/step - loss: 100581.1094 - mae: 166.8320\n",
      "Epoch 10/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 1ms/step - loss: 91799.0547 - mae: 161.4151\n",
      "Epoch 11/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 1ms/step - loss: 117338.0391 - mae: 182.3425\n",
      "Epoch 12/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 1ms/step - loss: 111971.4609 - mae: 180.3421\n",
      "Epoch 13/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1ms/step - loss: 110826.1250 - mae: 178.5382\n",
      "Epoch 14/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 1ms/step - loss: 115422.9609 - mae: 180.8383\n",
      "Epoch 15/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1ms/step - loss: 112059.4375 - mae: 179.7154\n",
      "Epoch 16/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 1ms/step - loss: 108876.8438 - mae: 176.6783\n",
      "Epoch 17/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 937us/step - loss: 108413.6094 - mae: 174.0749\n",
      "Epoch 18/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 998us/step - loss: 110883.9688 - mae: 175.5961\n",
      "Epoch 19/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1ms/step - loss: 104357.3516 - mae: 174.0525\n",
      "Epoch 20/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 1ms/step - loss: 105306.6953 - mae: 175.5319\n",
      "Epoch 21/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 1ms/step - loss: 119371.5859 - mae: 186.3596\n",
      "Epoch 22/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 1ms/step - loss: 117287.5625 - mae: 182.9301\n",
      "Epoch 23/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 2ms/step - loss: 118277.3594 - mae: 186.9639\n",
      "Epoch 24/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 1ms/step - loss: 122875.2031 - mae: 193.0616\n",
      "Epoch 25/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 1ms/step - loss: 118859.1094 - mae: 185.6440\n",
      "Epoch 26/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 2ms/step - loss: 114911.4844 - mae: 190.6782\n",
      "Epoch 27/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1ms/step - loss: 122473.7109 - mae: 193.8775\n",
      "Epoch 28/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 1ms/step - loss: 118881.7031 - mae: 188.5983\n",
      "Epoch 29/50\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[83], line 20\u001b[0m\n\u001b[0;32m     17\u001b[0m model\u001b[38;5;241m.\u001b[39mcompile(optimizer\u001b[38;5;241m=\u001b[39mAdam(learning_rate\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.001\u001b[39m), loss\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmse\u001b[39m\u001b[38;5;124m'\u001b[39m, metrics\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmae\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m     19\u001b[0m \u001b[38;5;66;03m# Train the model\u001b[39;00m\n\u001b[1;32m---> 20\u001b[0m history \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mfit(dummytrain, y,epochs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m50\u001b[39m,batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m32\u001b[39m,verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m     22\u001b[0m \u001b[38;5;66;03m# Predict on test data\u001b[39;00m\n\u001b[0;32m     23\u001b[0m test_predictions \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mpredict(dummytest)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 117\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py:317\u001b[0m, in \u001b[0;36mTensorFlowTrainer.fit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001b[0m\n\u001b[0;32m    315\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m epoch_iterator\u001b[38;5;241m.\u001b[39mcatch_stop_iteration():\n\u001b[0;32m    316\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m step, iterator \u001b[38;5;129;01min\u001b[39;00m epoch_iterator\u001b[38;5;241m.\u001b[39menumerate_epoch():\n\u001b[1;32m--> 317\u001b[0m         callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m    318\u001b[0m         logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_function(iterator)\n\u001b[0;32m    319\u001b[0m         logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pythonify_logs(logs)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\keras\\src\\callbacks\\callback_list.py:98\u001b[0m, in \u001b[0;36mCallbackList.on_train_batch_begin\u001b[1;34m(self, batch, logs)\u001b[0m\n\u001b[0;32m     95\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m callback \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallbacks:\n\u001b[0;32m     96\u001b[0m         callback\u001b[38;5;241m.\u001b[39mon_epoch_end(epoch, logs)\n\u001b[1;32m---> 98\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mon_train_batch_begin\u001b[39m(\u001b[38;5;28mself\u001b[39m, batch, logs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m     99\u001b[0m     logs \u001b[38;5;241m=\u001b[39m logs \u001b[38;5;129;01mor\u001b[39;00m {}\n\u001b[0;32m    100\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m callback \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallbacks:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Scale features using StandardScaler\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "# Build the Neural Network\n",
    "model = Sequential([\n",
    "    Dense(128, activation='relu', input_dim=dummytrain.shape[1]),  # Input Layer\n",
    "    Dropout(0.2),                                              # Dropout for regularization\n",
    "    Dense(64, activation='relu'),                              # Hidden Layer 1\n",
    "    Dropout(0.2),\n",
    "    Dense(32, activation='relu'),                              # Hidden Layer 2\n",
    "    Dense(1, activation='linear')                              # Output Layer for regression\n",
    "])\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer=Adam(learning_rate=0.001), loss='mse', metrics=['mae'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(dummytrain, y,epochs=50,batch_size=32,verbose=1)\n",
    "\n",
    "# Predict on test data\n",
    "test_predictions = model.predict(dummytest)\n",
    "\n",
    "submission_nn = pd.DataFrame({\"Orders_Count\":test_predictions})\n",
    "submission_nn.to_csv(\"NNModel.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "97f4b945",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1ms/step - loss: 163539.9062 - mae: 217.2480\n",
      "Epoch 2/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1ms/step - loss: 105468.5859 - mae: 171.5666\n",
      "Epoch 3/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 1ms/step - loss: 100916.3125 - mae: 165.2035\n",
      "Epoch 4/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 1ms/step - loss: 98905.0547 - mae: 163.1043\n",
      "Epoch 5/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 1ms/step - loss: 93150.6719 - mae: 160.4221\n",
      "Epoch 6/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 2ms/step - loss: 93582.9375 - mae: 160.0988\n",
      "Epoch 7/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 1ms/step - loss: 90542.2109 - mae: 157.0329\n",
      "Epoch 8/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 1ms/step - loss: 87026.1094 - mae: 155.6752\n",
      "Epoch 9/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1ms/step - loss: 87634.2031 - mae: 155.4136\n",
      "Epoch 10/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1ms/step - loss: 87398.5234 - mae: 153.8253\n",
      "Epoch 11/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1ms/step - loss: 84246.7656 - mae: 151.2870\n",
      "Epoch 12/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 1ms/step - loss: 88390.5234 - mae: 154.5559\n",
      "Epoch 13/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 1ms/step - loss: 82569.1172 - mae: 151.4777\n",
      "Epoch 14/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 1ms/step - loss: 83789.0312 - mae: 151.5221\n",
      "Epoch 15/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 2ms/step - loss: 80684.7734 - mae: 149.5695\n",
      "Epoch 16/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 2ms/step - loss: 81282.1328 - mae: 148.9036\n",
      "Epoch 17/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2ms/step - loss: 81794.7578 - mae: 150.7546\n",
      "Epoch 18/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2ms/step - loss: 78901.4375 - mae: 149.4861\n",
      "Epoch 19/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2ms/step - loss: 80319.9141 - mae: 149.1959\n",
      "Epoch 20/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 2ms/step - loss: 80644.0781 - mae: 149.6585\n",
      "Epoch 21/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 2ms/step - loss: 78194.4297 - mae: 148.2985\n",
      "Epoch 22/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 1ms/step - loss: 80093.4141 - mae: 150.0049\n",
      "Epoch 23/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 84635.4141 - mae: 152.5620\n",
      "Epoch 24/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 2ms/step - loss: 80915.1094 - mae: 150.3524\n",
      "Epoch 25/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 2ms/step - loss: 79258.9375 - mae: 150.0320\n",
      "Epoch 26/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 2ms/step - loss: 82365.8047 - mae: 149.5855\n",
      "Epoch 27/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 2ms/step - loss: 77120.8203 - mae: 147.4731\n",
      "Epoch 28/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 2ms/step - loss: 79138.4297 - mae: 147.7820\n",
      "Epoch 29/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 2ms/step - loss: 78427.4453 - mae: 146.8093\n",
      "Epoch 30/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 79205.4531 - mae: 147.2433\n",
      "Epoch 31/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 2ms/step - loss: 79359.8125 - mae: 149.8783\n",
      "Epoch 32/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 2ms/step - loss: 79208.8828 - mae: 148.7316\n",
      "Epoch 33/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 2ms/step - loss: 76463.8672 - mae: 146.9132\n",
      "Epoch 34/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 2ms/step - loss: 79257.1562 - mae: 148.8734\n",
      "Epoch 35/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 1ms/step - loss: 79156.5000 - mae: 148.1212\n",
      "Epoch 36/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 1ms/step - loss: 78667.2656 - mae: 148.2170\n",
      "Epoch 37/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 1ms/step - loss: 78882.6250 - mae: 147.3555\n",
      "Epoch 38/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 81579.1641 - mae: 149.8874\n",
      "Epoch 39/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 2ms/step - loss: 81129.2031 - mae: 148.1101\n",
      "Epoch 40/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 2ms/step - loss: 79564.3438 - mae: 147.5177\n",
      "Epoch 41/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 2ms/step - loss: 78673.6406 - mae: 147.1880\n",
      "Epoch 42/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 1ms/step - loss: 75902.1562 - mae: 146.7387\n",
      "Epoch 43/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 2ms/step - loss: 76016.3594 - mae: 145.5754\n",
      "Epoch 44/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 2ms/step - loss: 76692.5859 - mae: 145.7442\n",
      "Epoch 45/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2ms/step - loss: 77027.8906 - mae: 146.7419\n",
      "Epoch 46/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2ms/step - loss: 76351.5625 - mae: 145.9308\n",
      "Epoch 47/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 2ms/step - loss: 75621.0703 - mae: 145.3087\n",
      "Epoch 48/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 2ms/step - loss: 78518.5312 - mae: 146.4953\n",
      "Epoch 49/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 2ms/step - loss: 75494.7812 - mae: 145.1521\n",
      "Epoch 50/50\n",
      "\u001b[1m10045/10045\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 2ms/step - loss: 76396.2266 - mae: 145.1578\n",
      "\u001b[1m3690/3690\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 982us/step\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Per-column arrays must each be 1-dimensional",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[84], line 27\u001b[0m\n\u001b[0;32m     24\u001b[0m \u001b[38;5;66;03m# Predict on test data\u001b[39;00m\n\u001b[0;32m     25\u001b[0m test_predictions \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mpredict(dummytest)\n\u001b[1;32m---> 27\u001b[0m submission_nn \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOrders_Count\u001b[39m\u001b[38;5;124m\"\u001b[39m:test_predictions})\n\u001b[0;32m     28\u001b[0m submission_nn\u001b[38;5;241m.\u001b[39mto_csv(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNNModel.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m, index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:733\u001b[0m, in \u001b[0;36mDataFrame.__init__\u001b[1;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[0;32m    727\u001b[0m     mgr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_mgr(\n\u001b[0;32m    728\u001b[0m         data, axes\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mindex\u001b[39m\u001b[38;5;124m\"\u001b[39m: index, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolumns\u001b[39m\u001b[38;5;124m\"\u001b[39m: columns}, dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39mcopy\n\u001b[0;32m    729\u001b[0m     )\n\u001b[0;32m    731\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, \u001b[38;5;28mdict\u001b[39m):\n\u001b[0;32m    732\u001b[0m     \u001b[38;5;66;03m# GH#38939 de facto copy defaults to False only in non-dict cases\u001b[39;00m\n\u001b[1;32m--> 733\u001b[0m     mgr \u001b[38;5;241m=\u001b[39m dict_to_mgr(data, index, columns, dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39mcopy, typ\u001b[38;5;241m=\u001b[39mmanager)\n\u001b[0;32m    734\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, ma\u001b[38;5;241m.\u001b[39mMaskedArray):\n\u001b[0;32m    735\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mma\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m mrecords\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\construction.py:503\u001b[0m, in \u001b[0;36mdict_to_mgr\u001b[1;34m(data, index, columns, dtype, typ, copy)\u001b[0m\n\u001b[0;32m    499\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    500\u001b[0m         \u001b[38;5;66;03m# dtype check to exclude e.g. range objects, scalars\u001b[39;00m\n\u001b[0;32m    501\u001b[0m         arrays \u001b[38;5;241m=\u001b[39m [x\u001b[38;5;241m.\u001b[39mcopy() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(x, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m x \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m arrays]\n\u001b[1;32m--> 503\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m arrays_to_mgr(arrays, columns, index, dtype\u001b[38;5;241m=\u001b[39mdtype, typ\u001b[38;5;241m=\u001b[39mtyp, consolidate\u001b[38;5;241m=\u001b[39mcopy)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\construction.py:114\u001b[0m, in \u001b[0;36marrays_to_mgr\u001b[1;34m(arrays, columns, index, dtype, verify_integrity, typ, consolidate)\u001b[0m\n\u001b[0;32m    111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m verify_integrity:\n\u001b[0;32m    112\u001b[0m     \u001b[38;5;66;03m# figure out the index, if necessary\u001b[39;00m\n\u001b[0;32m    113\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 114\u001b[0m         index \u001b[38;5;241m=\u001b[39m _extract_index(arrays)\n\u001b[0;32m    115\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    116\u001b[0m         index \u001b[38;5;241m=\u001b[39m ensure_index(index)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\construction.py:664\u001b[0m, in \u001b[0;36m_extract_index\u001b[1;34m(data)\u001b[0m\n\u001b[0;32m    662\u001b[0m         raw_lengths\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28mlen\u001b[39m(val))\n\u001b[0;32m    663\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(val, np\u001b[38;5;241m.\u001b[39mndarray) \u001b[38;5;129;01mand\u001b[39;00m val\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 664\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPer-column arrays must each be 1-dimensional\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    666\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m indexes \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m raw_lengths:\n\u001b[0;32m    667\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIf using all scalar values, you must pass an index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mValueError\u001b[0m: Per-column arrays must each be 1-dimensional"
     ]
    }
   ],
   "source": [
    "# Build the Neural Network - Batch Normalization & Early Stopping\n",
    "from tensorflow.keras.layers import BatchNormalization\n",
    "\n",
    "model = Sequential([\n",
    "    Dense(128, activation='relu', input_dim=dummytrain.shape[1]),  # Input Layer\n",
    "    BatchNormalization(),\n",
    "    Dropout(0.3),                                              # Dropout for regularization\n",
    "    Dense(64, activation='relu'),                              # Hidden Layer 1\n",
    "    BatchNormalization(),\n",
    "    Dropout(0.3),\n",
    "    Dense(32, activation='relu'),                              # Hidden Layer 2\n",
    "    Dense(1, activation='linear')                              # Output Layer for regression\n",
    "])\n",
    "\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer=Adam(learning_rate=0.0001), loss='mse', metrics=['mae'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(dummytrain, y,epochs=50,batch_size=32,verbose=1, callbacks=[early_stopping])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "a3bcc96b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m3690/3690\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 887us/step\n"
     ]
    }
   ],
   "source": [
    "# Predict on test data\n",
    "test_predictions = model.predict(dummytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "38da0b0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_nn = pd.DataFrame(test_predictions, columns = [\"Orders_Count\"]) # RMSE: 1915\n",
    "submission_nn.to_csv(\"NNModelNew.csv\", index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d353137",
   "metadata": {},
   "source": [
    "### Tune Random Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "7f46febb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 50 candidates, totalling 150 fits\n",
      "Best Parameters from Randomized Search:\n",
      "{'n_estimators': 300, 'min_samples_split': 10, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 30, 'bootstrap': True}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "\n",
    "param_dist = {\n",
    "    'n_estimators': [50, 100, 200, 300],           # Number of trees\n",
    "    'max_depth': [None, 10, 20, 30, 40],           # Maximum depth of trees\n",
    "    'min_samples_split': [2, 5, 10],               # Minimum samples required to split\n",
    "    'min_samples_leaf': [1, 2, 4],                 # Minimum samples required in a leaf\n",
    "    'max_features': ['auto', 'sqrt', 'log2'],      # Number of features to consider\n",
    "    'bootstrap': [True, False]                     # Bootstrap sampling\n",
    "}\n",
    "\n",
    "# Randomized Search\n",
    "random_search = RandomizedSearchCV(\n",
    "    estimator=RandomForestRegressor(random_state=42),\n",
    "    param_distributions=param_dist,\n",
    "    n_iter=50,  # Number of configurations to try\n",
    "    scoring='neg_mean_squared_error',  # Evaluation metric\n",
    "    cv=3,  # Cross-validation splits\n",
    "    verbose=2,\n",
    "    random_state=42,\n",
    "    n_jobs=-1  # Use all processors\n",
    ")\n",
    "\n",
    "random_search.fit(dummytrain, y)\n",
    "\n",
    "# Best parameters from Randomized Search\n",
    "print(\"Best Parameters from Randomized Search:\")\n",
    "print(random_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "c68da588",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 32 candidates, totalling 96 fits\n",
      "Best Parameters from Grid Search:\n",
      "{'bootstrap': True, 'max_depth': 20, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 200}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = {\n",
    "    'n_estimators': [100, 200],                   # Refine based on Randomized Search\n",
    "    'max_depth': [10, 20],                        # Refine based on Randomized Search\n",
    "    'min_samples_split': [5, 10],                 # Refine based on Randomized Search\n",
    "    'min_samples_leaf': [1, 2],                   # Refine based on Randomized Search\n",
    "    'max_features': ['auto', 'sqrt'],             # Refine based on Randomized Search\n",
    "    'bootstrap': [True]                           # Fix based on Randomized Search\n",
    "}\n",
    "\n",
    "# Grid Search\n",
    "grid_search = GridSearchCV(\n",
    "    estimator=RandomForestRegressor(random_state=42),\n",
    "    param_grid=param_grid,\n",
    "    scoring='neg_mean_squared_error',\n",
    "    cv=3,\n",
    "    verbose=2,\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "grid_search.fit(dummytrain, y)\n",
    "\n",
    "# Best parameters from Grid Search\n",
    "print(\"Best Parameters from Grid Search:\")\n",
    "print(grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "674b9f77",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using Randomized Search CV Results to build Tuned Random Forest Model\n",
    "rf = RandomForestRegressor(n_estimators=300, min_samples_split=10, min_samples_leaf=1,\n",
    "                          max_features=\"sqrt\", max_depth=30, bootstrap=True)\n",
    "\n",
    "pred_rf = rf.fit(dummytrain, y).predict(dummytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "77c93d6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission2 = pd.DataFrame({\"Orders_Count\":pred_rf})\n",
    "submission2.to_csv(\"RFTUNEDModel.csv\", index = False) #212"
   ]
  }
 ],
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