DataStorm-4.0 Outlet duplicate predicter.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Nessesory Libries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all the nessecery libries for machine learning project\n",
    "import pandas as pd"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Data Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import data set\n",
    "outlet_data=pd.read_csv('Data Storn - Semi final data sets/outlets_data.csv')\n",
    "freezer_data=pd.read_csv('Data Storn - Semi final data sets/freezer_data.csv')\n",
    "product_data=pd.read_csv('Data Storn - Semi final data sets/product_data.csv')\n",
    "sales_data=pd.read_csv('Data Storn - Semi final data sets/sales_data.csv')\n",
    "week_data=pd.read_csv('Data Storn - Semi final data sets/week_data.csv')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Outlet_ID</th>\n",
       "      <th>area(sqft)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID7203</td>\n",
       "      <td>5660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID1878</td>\n",
       "      <td>4138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID1740</td>\n",
       "      <td>5546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID4366</td>\n",
       "      <td>5134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID8358</td>\n",
       "      <td>4100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>ID4119</td>\n",
       "      <td>3858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>ID2978</td>\n",
       "      <td>3495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>ID6549</td>\n",
       "      <td>1141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>ID1210</td>\n",
       "      <td>3322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>ID8207</td>\n",
       "      <td>3539</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>988 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Outlet_ID  area(sqft)\n",
       "0      ID7203        5660\n",
       "1      ID1878        4138\n",
       "2      ID1740        5546\n",
       "3      ID4366        5134\n",
       "4      ID8358        4100\n",
       "..        ...         ...\n",
       "983    ID4119        3858\n",
       "984    ID2978        3495\n",
       "985    ID6549        1141\n",
       "986    ID1210        3322\n",
       "987    ID8207        3539\n",
       "\n",
       "[988 rows x 2 columns]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlet_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outlet_ID     0\n",
      "area(sqft)    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# get count of null values in each column\n",
    "null_counts = outlet_data.isnull().sum()\n",
    "# print the counts\n",
    "print(null_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pid</th>\n",
       "      <th>volume</th>\n",
       "      <th>product_name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IP1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Vanilla Mini Cone</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IP2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Chocolate Petite Bar</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IP3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Strawberry Tiny Cup</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IP4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Mint Bite-size Pop</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IP5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Butter Pecan Small Stick</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BP1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Chocolate Fudge Big Scoop</td>\n",
       "      <td>1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BP2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Moose Tracks Jumbo Cup</td>\n",
       "      <td>1100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BP3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mint Chocolate Chip Family Pack</td>\n",
       "      <td>1150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>BP4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rocky Road Mega Tub</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BP5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Cookies and Cream Mega Tub</td>\n",
       "      <td>1100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pid  volume                     product_name  price\n",
       "0  IP1     0.2                Vanilla Mini Cone    100\n",
       "1  IP2     0.2             Chocolate Petite Bar     90\n",
       "2  IP3     0.2              Strawberry Tiny Cup    110\n",
       "3  IP4     0.2               Mint Bite-size Pop    100\n",
       "4  IP5     0.2         Butter Pecan Small Stick    100\n",
       "5  BP1     1.0        Chocolate Fudge Big Scoop   1200\n",
       "6  BP2     1.0           Moose Tracks Jumbo Cup   1100\n",
       "7  BP3     1.0  Mint Chocolate Chip Family Pack   1150\n",
       "8  BP4     1.0              Rocky Road Mega Tub   1000\n",
       "9  BP5     1.0       Cookies and Cream Mega Tub   1100"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Outlet_ID</th>\n",
       "      <th>week</th>\n",
       "      <th>pid</th>\n",
       "      <th>product_name</th>\n",
       "      <th>no_units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID7203</td>\n",
       "      <td>1</td>\n",
       "      <td>IP1</td>\n",
       "      <td>Vanilla Mini Cone</td>\n",
       "      <td>874.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID7203</td>\n",
       "      <td>1</td>\n",
       "      <td>IP2</td>\n",
       "      <td>Chocolate Petite Bar</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID7203</td>\n",
       "      <td>1</td>\n",
       "      <td>IP3</td>\n",
       "      <td>Strawberry Tiny Cup</td>\n",
       "      <td>1198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID7203</td>\n",
       "      <td>1</td>\n",
       "      <td>IP4</td>\n",
       "      <td>Mint Bite-size Pop</td>\n",
       "      <td>502.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID7203</td>\n",
       "      <td>1</td>\n",
       "      <td>IP5</td>\n",
       "      <td>Butter Pecan Small Stick</td>\n",
       "      <td>710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80995</th>\n",
       "      <td>ID8207</td>\n",
       "      <td>12</td>\n",
       "      <td>BP1</td>\n",
       "      <td>Chocolate Fudge Big Scoop</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80996</th>\n",
       "      <td>ID8207</td>\n",
       "      <td>12</td>\n",
       "      <td>BP2</td>\n",
       "      <td>Moose Tracks Jumbo Cup</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80997</th>\n",
       "      <td>ID8207</td>\n",
       "      <td>12</td>\n",
       "      <td>BP3</td>\n",
       "      <td>Mint Chocolate Chip Family Pack</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80998</th>\n",
       "      <td>ID8207</td>\n",
       "      <td>12</td>\n",
       "      <td>BP4</td>\n",
       "      <td>Rocky Road Mega Tub</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80999</th>\n",
       "      <td>ID8207</td>\n",
       "      <td>12</td>\n",
       "      <td>BP5</td>\n",
       "      <td>Cookies and Cream Mega Tub</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Outlet_ID  week  pid                     product_name  no_units\n",
       "0        ID7203     1  IP1                Vanilla Mini Cone     874.0\n",
       "1        ID7203     1  IP2             Chocolate Petite Bar     105.0\n",
       "2        ID7203     1  IP3              Strawberry Tiny Cup    1198.0\n",
       "3        ID7203     1  IP4               Mint Bite-size Pop     502.0\n",
       "4        ID7203     1  IP5         Butter Pecan Small Stick     710.0\n",
       "...         ...   ...  ...                              ...       ...\n",
       "80995    ID8207    12  BP1        Chocolate Fudge Big Scoop      56.0\n",
       "80996    ID8207    12  BP2           Moose Tracks Jumbo Cup      60.0\n",
       "80997    ID8207    12  BP3  Mint Chocolate Chip Family Pack      51.0\n",
       "80998    ID8207    12  BP4              Rocky Road Mega Tub      78.0\n",
       "80999    ID8207    12  BP5       Cookies and Cream Mega Tub      73.0\n",
       "\n",
       "[81000 rows x 5 columns]"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outlet_ID       0\n",
      "week            0\n",
      "pid             0\n",
      "product_name    0\n",
      "no_units        0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# get count of null values in each column\n",
    "null_counts = sales_data.isnull().sum()\n",
    "# print the counts\n",
    "print(null_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Week</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>End Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Week 1</td>\n",
       "      <td>1/2/2023</td>\n",
       "      <td>1/8/2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Week 2</td>\n",
       "      <td>1/9/2023</td>\n",
       "      <td>1/15/2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Week 3</td>\n",
       "      <td>1/16/2023</td>\n",
       "      <td>1/22/2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Week 4</td>\n",
       "      <td>1/23/2023</td>\n",
       "      <td>1/29/2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Week 5</td>\n",
       "      <td>1/30/2023</td>\n",
       "      <td>2/5/2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Week 6</td>\n",
       "      <td>2/6/2023</td>\n",
       "      <td>2/12/2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Week 7</td>\n",
       "      <td>2/13/2023</td>\n",
       "      <td>2/19/2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Week 8</td>\n",
       "      <td>2/20/2023</td>\n",
       "      <td>2/26/2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Week 9</td>\n",
       "      <td>2/27/2023</td>\n",
       "      <td>3/5/2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Week 10</td>\n",
       "      <td>3/6/2023</td>\n",
       "      <td>3/12/2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Week 11</td>\n",
       "      <td>3/13/2023</td>\n",
       "      <td>3/19/2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Week 12</td>\n",
       "      <td>3/20/2023</td>\n",
       "      <td>3/26/2023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Week Start Date   End Date\n",
       "0    Week 1   1/2/2023   1/8/2023\n",
       "1    Week 2   1/9/2023  1/15/2023\n",
       "2    Week 3  1/16/2023  1/22/2023\n",
       "3    Week 4  1/23/2023  1/29/2023\n",
       "4    Week 5  1/30/2023   2/5/2023\n",
       "5    Week 6   2/6/2023  2/12/2023\n",
       "6    Week 7  2/13/2023  2/19/2023\n",
       "7    Week 8  2/20/2023  2/26/2023\n",
       "8    Week 9  2/27/2023   3/5/2023\n",
       "9   Week 10   3/6/2023  3/12/2023\n",
       "10  Week 11  3/13/2023  3/19/2023\n",
       "11  Week 12  3/20/2023  3/26/2023"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Week          0\n",
      "Start Date    0\n",
      "End Date      0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# get count of null values in each column\n",
    "null_counts = week_data.isnull().sum()\n",
    "# print the counts\n",
    "print(null_counts)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating Dtaframes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### create a data frame for each outlate and product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a data frame for each outlate and product\n",
    "sales_grouped_outlats = sales_data.groupby(['Outlet_ID', 'pid'])['no_units'].sum().reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Outlet_ID</th>\n",
       "      <th>pid</th>\n",
       "      <th>no_units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID1008</td>\n",
       "      <td>IP1</td>\n",
       "      <td>6970.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>ID1008</td>\n",
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       "      <td>6285.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>ID1008</td>\n",
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       "      <th>4</th>\n",
       "      <td>ID1008</td>\n",
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       "    <tr>\n",
       "      <th>7185</th>\n",
       "      <td>ID9980</td>\n",
       "      <td>IP1</td>\n",
       "      <td>6212.0</td>\n",
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       "    <tr>\n",
       "      <th>7186</th>\n",
       "      <td>ID9980</td>\n",
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       "      <th>7187</th>\n",
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       "    <tr>\n",
       "      <th>7188</th>\n",
       "      <td>ID9980</td>\n",
       "      <td>IP4</td>\n",
       "      <td>9370.0</td>\n",
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       "    <tr>\n",
       "      <th>7189</th>\n",
       "      <td>ID9980</td>\n",
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       "      <td>8990.0</td>\n",
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       "<p>7190 rows × 3 columns</p>\n",
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       "     Outlet_ID  pid  no_units\n",
       "0       ID1008  IP1    6970.0\n",
       "1       ID1008  IP2    7206.0\n",
       "2       ID1008  IP3    6285.0\n",
       "3       ID1008  IP4    6486.0\n",
       "4       ID1008  IP5    6055.0\n",
       "...        ...  ...       ...\n",
       "7185    ID9980  IP1    6212.0\n",
       "7186    ID9980  IP2    7888.0\n",
       "7187    ID9980  IP3    4999.0\n",
       "7188    ID9980  IP4    9370.0\n",
       "7189    ID9980  IP5    8990.0\n",
       "\n",
       "[7190 rows x 3 columns]"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_grouped_outlats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pivot the dataframe to make item_description values as columns\n",
    "pivoted_df_sales = sales_grouped_outlats.pivot(index='Outlet_ID', columns='pid', values='no_units')\n",
    "\n",
    "# fill NaN values with 0\n",
    "pivoted_df_sales.fillna(0, inplace=True)\n",
    "\n",
    "# resetting the index\n",
    "pivoted_df_sales = pivoted_df_sales.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.0</td>\n",
       "      <td>6390.0</td>\n",
       "      <td>6784.0</td>\n",
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       "      <td>1870.0</td>\n",
       "      <td>7341.0</td>\n",
       "      <td>6473.0</td>\n",
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       "pid Outlet_ID     BP1     BP2     BP3     BP4     BP5     IP1     IP2     IP3  \\\n",
       "0      ID1008     0.0     0.0     0.0     0.0     0.0  6970.0  7206.0  6285.0   \n",
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       "2      ID1024  1703.0  1251.0  1150.0  1388.0  1451.0     0.0     0.0     0.0   \n",
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       "4      ID1036   567.0   583.0   725.0   564.0   722.0  4934.0  5708.0  6200.0   \n",
       "..        ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "946    ID9960  1815.0  1878.0  1359.0  1958.0  2257.0  7142.0  6123.0  8809.0   \n",
       "947    ID9971     0.0     0.0     0.0     0.0     0.0  6390.0  6784.0  7786.0   \n",
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       "949    ID9979     0.0     0.0     0.0     0.0     0.0  5596.0  7372.0  6283.0   \n",
       "950    ID9980  2462.0  2094.0  2374.0  2435.0  1871.0  6212.0  7888.0  4999.0   \n",
       "\n",
       "pid     IP4      IP5  \n",
       "0    6486.0   6055.0  \n",
       "1       0.0      0.0  \n",
       "2       0.0      0.0  \n",
       "3    7961.0   6918.0  \n",
       "4    5526.0   5835.0  \n",
       "..      ...      ...  \n",
       "946  5699.0  11328.0  \n",
       "947  9162.0   6361.0  \n",
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     "execution_count": 193,
     "metadata": {},
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   "source": [
    "pivoted_df_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add columns for each product to the outlet data frame\n",
    "\n",
    "sales_grouped_outlats=(pd.merge(outlet_data, pivoted_df_sales, on='Outlet_ID', how='left'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
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       "      <td>611.0</td>\n",
       "      <td>677.0</td>\n",
       "      <td>716.0</td>\n",
       "      <td>543.0</td>\n",
       "      <td>4932.0</td>\n",
       "      <td>6148.0</td>\n",
       "      <td>4826.0</td>\n",
       "      <td>5844.0</td>\n",
       "      <td>5935.0</td>\n",
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       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>ID1210</td>\n",
       "      <td>3322</td>\n",
       "      <td>519.0</td>\n",
       "      <td>532.0</td>\n",
       "      <td>664.0</td>\n",
       "      <td>608.0</td>\n",
       "      <td>677.0</td>\n",
       "      <td>4745.0</td>\n",
       "      <td>7020.0</td>\n",
       "      <td>5510.0</td>\n",
       "      <td>5246.0</td>\n",
       "      <td>5113.0</td>\n",
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       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>ID8207</td>\n",
       "      <td>3539</td>\n",
       "      <td>599.0</td>\n",
       "      <td>694.0</td>\n",
       "      <td>683.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>619.0</td>\n",
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       "      <td>4484.0</td>\n",
       "      <td>4285.0</td>\n",
       "      <td>6164.0</td>\n",
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       "  </tbody>\n",
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       "<p>988 rows × 12 columns</p>\n",
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       "    Outlet_ID  area(sqft)    BP1    BP2    BP3    BP4    BP5     IP1     IP2  \\\n",
       "0      ID7203        5660    0.0    0.0    0.0    0.0    0.0  6361.0  7295.0   \n",
       "1      ID1878        4138    0.0    0.0    0.0    0.0    0.0  7860.0  6622.0   \n",
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       "4      ID8358        4100    0.0    0.0    0.0    0.0    0.0  7057.0  9562.0   \n",
       "..        ...         ...    ...    ...    ...    ...    ...     ...     ...   \n",
       "983    ID4119        3858  624.0  508.0  631.0  602.0  573.0  4179.0  5104.0   \n",
       "984    ID2978        3495  634.0  503.0  655.0  682.0  679.0  4092.0  5710.0   \n",
       "985    ID6549        1141  742.0  611.0  677.0  716.0  543.0  4932.0  6148.0   \n",
       "986    ID1210        3322  519.0  532.0  664.0  608.0  677.0  4745.0  7020.0   \n",
       "987    ID8207        3539  599.0  694.0  683.0  565.0  619.0  6379.0  4611.0   \n",
       "\n",
       "        IP3     IP4     IP5  \n",
       "0    9127.0  5520.0  6443.0  \n",
       "1    6856.0  7954.0  6748.0  \n",
       "2    7837.0  7192.0  6817.0  \n",
       "3    9232.0  8759.0  9524.0  \n",
       "4    7652.0  6241.0  8866.0  \n",
       "..      ...     ...     ...  \n",
       "983  3951.0  4503.0  6038.0  \n",
       "984  4934.0  5006.0  4800.0  \n",
       "985  4826.0  5844.0  5935.0  \n",
       "986  5510.0  5246.0  5113.0  \n",
       "987  4484.0  4285.0  6164.0  \n",
       "\n",
       "[988 rows x 12 columns]"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_grouped_outlats"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outliers"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### creating fetures for total item solds for bulk and impuls item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_grouped_outlats['tot_bulk_itm'] = sales_grouped_outlats[['BP1', 'BP2', 'BP3', 'BP4', 'BP5']].sum(axis=1)\n",
    "sales_grouped_outlats['tot_impulse_itm'] = sales_grouped_outlats[['IP1', 'IP2', 'IP3', 'IP4', 'IP5']].sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
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       "      <td>7057.0</td>\n",
       "      <td>9562.0</td>\n",
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       "      <td>608.0</td>\n",
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       "      <td>5510.0</td>\n",
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       "      <td>5113.0</td>\n",
       "      <td>3000.0</td>\n",
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       "      <th>987</th>\n",
       "      <td>ID8207</td>\n",
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       "      <td>599.0</td>\n",
       "      <td>694.0</td>\n",
       "      <td>683.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>619.0</td>\n",
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       "      <td>6164.0</td>\n",
       "      <td>3160.0</td>\n",
       "      <td>25923.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>988 rows × 14 columns</p>\n",
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      "text/plain": [
       "    Outlet_ID  area(sqft)    BP1    BP2    BP3    BP4    BP5     IP1     IP2  \\\n",
       "0      ID7203        5660    0.0    0.0    0.0    0.0    0.0  6361.0  7295.0   \n",
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       "..        ...         ...    ...    ...    ...    ...    ...     ...     ...   \n",
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       "986    ID1210        3322  519.0  532.0  664.0  608.0  677.0  4745.0  7020.0   \n",
       "987    ID8207        3539  599.0  694.0  683.0  565.0  619.0  6379.0  4611.0   \n",
       "\n",
       "        IP3     IP4     IP5  tot_bulk_itm  tot_impulse_itm  \n",
       "0    9127.0  5520.0  6443.0           0.0          34746.0  \n",
       "1    6856.0  7954.0  6748.0           0.0          36040.0  \n",
       "2    7837.0  7192.0  6817.0           0.0          37587.0  \n",
       "3    9232.0  8759.0  9524.0           0.0          40304.0  \n",
       "4    7652.0  6241.0  8866.0           0.0          39378.0  \n",
       "..      ...     ...     ...           ...              ...  \n",
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       "984  4934.0  5006.0  4800.0        3153.0          24542.0  \n",
       "985  4826.0  5844.0  5935.0        3289.0          27685.0  \n",
       "986  5510.0  5246.0  5113.0        3000.0          27634.0  \n",
       "987  4484.0  4285.0  6164.0        3160.0          25923.0  \n",
       "\n",
       "[988 rows x 14 columns]"
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     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_grouped_outlats"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creatng feature for Total Icecream Volume sold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_grouped_outlats['tot_icecream_vol']=(sales_grouped_outlats['tot_bulk_itm']*1.0)+(sales_grouped_outlats['tot_impulse_itm']*0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_grouped_outlats[['area(sqft)','BP1', 'BP2', 'BP3', 'BP4', 'BP5','IP1', 'IP2', 'IP3', 'IP4', 'IP5','tot_bulk_itm','tot_impulse_itm']] = sales_grouped_outlats[['area(sqft)','BP1', 'BP2', 'BP3', 'BP4', 'BP5','IP1', 'IP2', 'IP3', 'IP4', 'IP5','tot_bulk_itm','tot_impulse_itm']].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
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       "      <td>ID4366</td>\n",
       "      <td>5134</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6530</td>\n",
       "      <td>6259</td>\n",
       "      <td>9232</td>\n",
       "      <td>8759</td>\n",
       "      <td>9524</td>\n",
       "      <td>0</td>\n",
       "      <td>40304</td>\n",
       "      <td>8060.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID8358</td>\n",
       "      <td>4100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7057</td>\n",
       "      <td>9562</td>\n",
       "      <td>7652</td>\n",
       "      <td>6241</td>\n",
       "      <td>8866</td>\n",
       "      <td>0</td>\n",
       "      <td>39378</td>\n",
       "      <td>7875.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>ID4119</td>\n",
       "      <td>3858</td>\n",
       "      <td>624</td>\n",
       "      <td>508</td>\n",
       "      <td>631</td>\n",
       "      <td>602</td>\n",
       "      <td>573</td>\n",
       "      <td>4179</td>\n",
       "      <td>5104</td>\n",
       "      <td>3951</td>\n",
       "      <td>4503</td>\n",
       "      <td>6038</td>\n",
       "      <td>2938</td>\n",
       "      <td>23775</td>\n",
       "      <td>7693.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>ID2978</td>\n",
       "      <td>3495</td>\n",
       "      <td>634</td>\n",
       "      <td>503</td>\n",
       "      <td>655</td>\n",
       "      <td>682</td>\n",
       "      <td>679</td>\n",
       "      <td>4092</td>\n",
       "      <td>5710</td>\n",
       "      <td>4934</td>\n",
       "      <td>5006</td>\n",
       "      <td>4800</td>\n",
       "      <td>3153</td>\n",
       "      <td>24542</td>\n",
       "      <td>8061.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>ID6549</td>\n",
       "      <td>1141</td>\n",
       "      <td>742</td>\n",
       "      <td>611</td>\n",
       "      <td>677</td>\n",
       "      <td>716</td>\n",
       "      <td>543</td>\n",
       "      <td>4932</td>\n",
       "      <td>6148</td>\n",
       "      <td>4826</td>\n",
       "      <td>5844</td>\n",
       "      <td>5935</td>\n",
       "      <td>3289</td>\n",
       "      <td>27685</td>\n",
       "      <td>8826.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>ID1210</td>\n",
       "      <td>3322</td>\n",
       "      <td>519</td>\n",
       "      <td>532</td>\n",
       "      <td>664</td>\n",
       "      <td>608</td>\n",
       "      <td>677</td>\n",
       "      <td>4745</td>\n",
       "      <td>7020</td>\n",
       "      <td>5510</td>\n",
       "      <td>5246</td>\n",
       "      <td>5113</td>\n",
       "      <td>3000</td>\n",
       "      <td>27634</td>\n",
       "      <td>8526.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>ID8207</td>\n",
       "      <td>3539</td>\n",
       "      <td>599</td>\n",
       "      <td>694</td>\n",
       "      <td>683</td>\n",
       "      <td>565</td>\n",
       "      <td>619</td>\n",
       "      <td>6379</td>\n",
       "      <td>4611</td>\n",
       "      <td>4484</td>\n",
       "      <td>4285</td>\n",
       "      <td>6164</td>\n",
       "      <td>3160</td>\n",
       "      <td>25923</td>\n",
       "      <td>8344.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>988 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Outlet_ID  area(sqft)  BP1  BP2  BP3  BP4  BP5   IP1   IP2   IP3   IP4  \\\n",
       "0      ID7203        5660    0    0    0    0    0  6361  7295  9127  5520   \n",
       "1      ID1878        4138    0    0    0    0    0  7860  6622  6856  7954   \n",
       "2      ID1740        5546    0    0    0    0    0  7625  8116  7837  7192   \n",
       "3      ID4366        5134    0    0    0    0    0  6530  6259  9232  8759   \n",
       "4      ID8358        4100    0    0    0    0    0  7057  9562  7652  6241   \n",
       "..        ...         ...  ...  ...  ...  ...  ...   ...   ...   ...   ...   \n",
       "983    ID4119        3858  624  508  631  602  573  4179  5104  3951  4503   \n",
       "984    ID2978        3495  634  503  655  682  679  4092  5710  4934  5006   \n",
       "985    ID6549        1141  742  611  677  716  543  4932  6148  4826  5844   \n",
       "986    ID1210        3322  519  532  664  608  677  4745  7020  5510  5246   \n",
       "987    ID8207        3539  599  694  683  565  619  6379  4611  4484  4285   \n",
       "\n",
       "      IP5  tot_bulk_itm  tot_impulse_itm  tot_icecream_vol  \n",
       "0    6443             0            34746            6949.2  \n",
       "1    6748             0            36040            7208.0  \n",
       "2    6817             0            37587            7517.4  \n",
       "3    9524             0            40304            8060.8  \n",
       "4    8866             0            39378            7875.6  \n",
       "..    ...           ...              ...               ...  \n",
       "983  6038          2938            23775            7693.0  \n",
       "984  4800          3153            24542            8061.4  \n",
       "985  5935          3289            27685            8826.0  \n",
       "986  5113          3000            27634            8526.8  \n",
       "987  6164          3160            25923            8344.6  \n",
       "\n",
       "[988 rows x 15 columns]"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_grouped_outlats"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "count the number of unique products sold per outlet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "# count the number of unique products sold per outlet\n",
    "num_products_df = sales_data.groupby('Outlet_ID')['pid'].nunique().reset_index()\n",
    "num_products_df.columns = ['Outlet_ID', 'Num_Products_Sold']\n",
    "\n",
    "# merge the new feature with the outlet data set\n",
    "sales_grouped_outlats = sales_grouped_outlats.merge(num_products_df, on='Outlet_ID', how='left')\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a feature for total revenew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "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>pid</th>\n",
       "      <th>volume</th>\n",
       "      <th>product_name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IP1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Vanilla Mini Cone</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IP2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Chocolate Petite Bar</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IP3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Strawberry Tiny Cup</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IP4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Mint Bite-size Pop</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IP5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Butter Pecan Small Stick</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BP1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Chocolate Fudge Big Scoop</td>\n",
       "      <td>1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BP2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Moose Tracks Jumbo Cup</td>\n",
       "      <td>1100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BP3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mint Chocolate Chip Family Pack</td>\n",
       "      <td>1150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>BP4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rocky Road Mega Tub</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BP5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Cookies and Cream Mega Tub</td>\n",
       "      <td>1100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pid  volume                     product_name  price\n",
       "0  IP1     0.2                Vanilla Mini Cone    100\n",
       "1  IP2     0.2             Chocolate Petite Bar     90\n",
       "2  IP3     0.2              Strawberry Tiny Cup    110\n",
       "3  IP4     0.2               Mint Bite-size Pop    100\n",
       "4  IP5     0.2         Butter Pecan Small Stick    100\n",
       "5  BP1     1.0        Chocolate Fudge Big Scoop   1200\n",
       "6  BP2     1.0           Moose Tracks Jumbo Cup   1100\n",
       "7  BP3     1.0  Mint Chocolate Chip Family Pack   1150\n",
       "8  BP4     1.0              Rocky Road Mega Tub   1000\n",
       "9  BP5     1.0       Cookies and Cream Mega Tub   1100"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_grouped_outlats['tot_revnew']=((sales_grouped_outlats['BP1']*1200)+(sales_grouped_outlats['BP2']*1100)+(sales_grouped_outlats['BP3']*1150)+(sales_grouped_outlats['BP4']*1000)+(sales_grouped_outlats['BP5']*1100)+(sales_grouped_outlats['IP1']*100)+(sales_grouped_outlats['IP2']*90)+(sales_grouped_outlats['IP3']*110)+(sales_grouped_outlats['IP4']*100)+(sales_grouped_outlats['IP5']*100)).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .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>Outlet_ID</th>\n",
       "      <th>area(sqft)</th>\n",
       "      <th>BP1</th>\n",
       "      <th>BP2</th>\n",
       "      <th>BP3</th>\n",
       "      <th>BP4</th>\n",
       "      <th>BP5</th>\n",
       "      <th>IP1</th>\n",
       "      <th>IP2</th>\n",
       "      <th>IP3</th>\n",
       "      <th>IP4</th>\n",
       "      <th>IP5</th>\n",
       "      <th>tot_bulk_itm</th>\n",
       "      <th>tot_impulse_itm</th>\n",
       "      <th>tot_icecream_vol</th>\n",
       "      <th>Num_Products_Sold</th>\n",
       "      <th>tot_revnew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID7203</td>\n",
       "      <td>5660</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6361</td>\n",
       "      <td>7295</td>\n",
       "      <td>9127</td>\n",
       "      <td>5520</td>\n",
       "      <td>6443</td>\n",
       "      <td>0</td>\n",
       "      <td>34746</td>\n",
       "      <td>6949.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3492920.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID1878</td>\n",
       "      <td>4138</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7860</td>\n",
       "      <td>6622</td>\n",
       "      <td>6856</td>\n",
       "      <td>7954</td>\n",
       "      <td>6748</td>\n",
       "      <td>0</td>\n",
       "      <td>36040</td>\n",
       "      <td>7208.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3606340.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID1740</td>\n",
       "      <td>5546</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7625</td>\n",
       "      <td>8116</td>\n",
       "      <td>7837</td>\n",
       "      <td>7192</td>\n",
       "      <td>6817</td>\n",
       "      <td>0</td>\n",
       "      <td>37587</td>\n",
       "      <td>7517.4</td>\n",
       "      <td>5</td>\n",
       "      <td>3755910.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID4366</td>\n",
       "      <td>5134</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6530</td>\n",
       "      <td>6259</td>\n",
       "      <td>9232</td>\n",
       "      <td>8759</td>\n",
       "      <td>9524</td>\n",
       "      <td>0</td>\n",
       "      <td>40304</td>\n",
       "      <td>8060.8</td>\n",
       "      <td>5</td>\n",
       "      <td>4060130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID8358</td>\n",
       "      <td>4100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7057</td>\n",
       "      <td>9562</td>\n",
       "      <td>7652</td>\n",
       "      <td>6241</td>\n",
       "      <td>8866</td>\n",
       "      <td>0</td>\n",
       "      <td>39378</td>\n",
       "      <td>7875.6</td>\n",
       "      <td>5</td>\n",
       "      <td>3918700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>ID4119</td>\n",
       "      <td>3858</td>\n",
       "      <td>624</td>\n",
       "      <td>508</td>\n",
       "      <td>631</td>\n",
       "      <td>602</td>\n",
       "      <td>573</td>\n",
       "      <td>4179</td>\n",
       "      <td>5104</td>\n",
       "      <td>3951</td>\n",
       "      <td>4503</td>\n",
       "      <td>6038</td>\n",
       "      <td>2938</td>\n",
       "      <td>23775</td>\n",
       "      <td>7693.0</td>\n",
       "      <td>10</td>\n",
       "      <td>5631520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>ID2978</td>\n",
       "      <td>3495</td>\n",
       "      <td>634</td>\n",
       "      <td>503</td>\n",
       "      <td>655</td>\n",
       "      <td>682</td>\n",
       "      <td>679</td>\n",
       "      <td>4092</td>\n",
       "      <td>5710</td>\n",
       "      <td>4934</td>\n",
       "      <td>5006</td>\n",
       "      <td>4800</td>\n",
       "      <td>3153</td>\n",
       "      <td>24542</td>\n",
       "      <td>8061.4</td>\n",
       "      <td>10</td>\n",
       "      <td>5942690.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>ID6549</td>\n",
       "      <td>1141</td>\n",
       "      <td>742</td>\n",
       "      <td>611</td>\n",
       "      <td>677</td>\n",
       "      <td>716</td>\n",
       "      <td>543</td>\n",
       "      <td>4932</td>\n",
       "      <td>6148</td>\n",
       "      <td>4826</td>\n",
       "      <td>5844</td>\n",
       "      <td>5935</td>\n",
       "      <td>3289</td>\n",
       "      <td>27685</td>\n",
       "      <td>8826.0</td>\n",
       "      <td>10</td>\n",
       "      <td>6409630.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>ID1210</td>\n",
       "      <td>3322</td>\n",
       "      <td>519</td>\n",
       "      <td>532</td>\n",
       "      <td>664</td>\n",
       "      <td>608</td>\n",
       "      <td>677</td>\n",
       "      <td>4745</td>\n",
       "      <td>7020</td>\n",
       "      <td>5510</td>\n",
       "      <td>5246</td>\n",
       "      <td>5113</td>\n",
       "      <td>3000</td>\n",
       "      <td>27634</td>\n",
       "      <td>8526.8</td>\n",
       "      <td>10</td>\n",
       "      <td>6072600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>ID8207</td>\n",
       "      <td>3539</td>\n",
       "      <td>599</td>\n",
       "      <td>694</td>\n",
       "      <td>683</td>\n",
       "      <td>565</td>\n",
       "      <td>619</td>\n",
       "      <td>6379</td>\n",
       "      <td>4611</td>\n",
       "      <td>4484</td>\n",
       "      <td>4285</td>\n",
       "      <td>6164</td>\n",
       "      <td>3160</td>\n",
       "      <td>25923</td>\n",
       "      <td>8344.6</td>\n",
       "      <td>10</td>\n",
       "      <td>6104580.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>988 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Outlet_ID  area(sqft)  BP1  BP2  BP3  BP4  BP5   IP1   IP2   IP3   IP4  \\\n",
       "0      ID7203        5660    0    0    0    0    0  6361  7295  9127  5520   \n",
       "1      ID1878        4138    0    0    0    0    0  7860  6622  6856  7954   \n",
       "2      ID1740        5546    0    0    0    0    0  7625  8116  7837  7192   \n",
       "3      ID4366        5134    0    0    0    0    0  6530  6259  9232  8759   \n",
       "4      ID8358        4100    0    0    0    0    0  7057  9562  7652  6241   \n",
       "..        ...         ...  ...  ...  ...  ...  ...   ...   ...   ...   ...   \n",
       "983    ID4119        3858  624  508  631  602  573  4179  5104  3951  4503   \n",
       "984    ID2978        3495  634  503  655  682  679  4092  5710  4934  5006   \n",
       "985    ID6549        1141  742  611  677  716  543  4932  6148  4826  5844   \n",
       "986    ID1210        3322  519  532  664  608  677  4745  7020  5510  5246   \n",
       "987    ID8207        3539  599  694  683  565  619  6379  4611  4484  4285   \n",
       "\n",
       "      IP5  tot_bulk_itm  tot_impulse_itm  tot_icecream_vol  Num_Products_Sold  \\\n",
       "0    6443             0            34746            6949.2                  5   \n",
       "1    6748             0            36040            7208.0                  5   \n",
       "2    6817             0            37587            7517.4                  5   \n",
       "3    9524             0            40304            8060.8                  5   \n",
       "4    8866             0            39378            7875.6                  5   \n",
       "..    ...           ...              ...               ...                ...   \n",
       "983  6038          2938            23775            7693.0                 10   \n",
       "984  4800          3153            24542            8061.4                 10   \n",
       "985  5935          3289            27685            8826.0                 10   \n",
       "986  5113          3000            27634            8526.8                 10   \n",
       "987  6164          3160            25923            8344.6                 10   \n",
       "\n",
       "     tot_revnew  \n",
       "0     3492920.0  \n",
       "1     3606340.0  \n",
       "2     3755910.0  \n",
       "3     4060130.0  \n",
       "4     3918700.0  \n",
       "..          ...  \n",
       "983   5631520.0  \n",
       "984   5942690.0  \n",
       "985   6409630.0  \n",
       "986   6072600.0  \n",
       "987   6104580.0  \n",
       "\n",
       "[988 rows x 17 columns]"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_grouped_outlats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sales_grouped_outlats = sales_grouped_outlats.drop(['BP1', 'BP2', 'BP3', 'BP4', 'BP5','IP1', 'IP2', 'IP3', 'IP4', 'IP5','tot_icecream_vol','tot_revnew'], axis=1)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vizualizing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1089919353.py:5: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  corr = sales_grouped_outlats.corr()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation Matrix')"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Compute correlation matrix\n",
    "corr = sales_grouped_outlats.corr()\n",
    "\n",
    "# Plot heatmap of correlation matrix\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(corr, annot=True, cmap='coolwarm')\n",
    "plt.title('Correlation Matrix')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove duplicates in outlets_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:7: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n",
      "  for name, group in groups.groupby(['Outlet_ID']):\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n",
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1673593869.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_of_duplicates=df_of_duplicates.append(group)\n"
     ]
    }
   ],
   "source": [
    "# Group the DataFrame by column 'Outlet_ID' and get the groups with duplicates\n",
    "groups = sales_grouped_outlats[['Outlet_ID','area(sqft)','tot_impulse_itm', 'tot_icecream_vol', 'tot_revnew']].groupby(['Outlet_ID']).filter(lambda x: len(x) > 1)\n",
    "\n",
    "df_of_duplicates=pd.DataFrame()\n",
    "\n",
    "# Iterate over the groups and print the duplicate rows\n",
    "for name, group in groups.groupby(['Outlet_ID']):\n",
    "    df_of_duplicates=df_of_duplicates.append(group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "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>Outlet_ID</th>\n",
       "      <th>area(sqft)</th>\n",
       "      <th>tot_impulse_itm</th>\n",
       "      <th>tot_icecream_vol</th>\n",
       "      <th>tot_revnew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>ID1365</td>\n",
       "      <td>5823</td>\n",
       "      <td>39285</td>\n",
       "      <td>11171.0</td>\n",
       "      <td>7603540.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>ID1365</td>\n",
       "      <td>1291</td>\n",
       "      <td>39285</td>\n",
       "      <td>11171.0</td>\n",
       "      <td>7603540.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>ID1429</td>\n",
       "      <td>1300</td>\n",
       "      <td>25391</td>\n",
       "      <td>11430.2</td>\n",
       "      <td>9571970.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>ID1429</td>\n",
       "      <td>2854</td>\n",
       "      <td>25391</td>\n",
       "      <td>11430.2</td>\n",
       "      <td>9571970.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>ID1485</td>\n",
       "      <td>4535</td>\n",
       "      <td>44778</td>\n",
       "      <td>24490.6</td>\n",
       "      <td>21663150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>ID9672</td>\n",
       "      <td>7966</td>\n",
       "      <td>52240</td>\n",
       "      <td>26425.0</td>\n",
       "      <td>22981250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>ID9841</td>\n",
       "      <td>578</td>\n",
       "      <td>25835</td>\n",
       "      <td>11691.0</td>\n",
       "      <td>9817490.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>ID9841</td>\n",
       "      <td>2179</td>\n",
       "      <td>25835</td>\n",
       "      <td>11691.0</td>\n",
       "      <td>9817490.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>ID9980</td>\n",
       "      <td>4256</td>\n",
       "      <td>37459</td>\n",
       "      <td>18727.8</td>\n",
       "      <td>16198010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>ID9980</td>\n",
       "      <td>7527</td>\n",
       "      <td>37459</td>\n",
       "      <td>18727.8</td>\n",
       "      <td>16198010.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Outlet_ID  area(sqft)  tot_impulse_itm  tot_icecream_vol  tot_revnew\n",
       "98     ID1365        5823            39285           11171.0   7603540.0\n",
       "494    ID1365        1291            39285           11171.0   7603540.0\n",
       "399    ID1429        1300            25391           11430.2   9571970.0\n",
       "851    ID1429        2854            25391           11430.2   9571970.0\n",
       "292    ID1485        4535            44778           24490.6  21663150.0\n",
       "..        ...         ...              ...               ...         ...\n",
       "576    ID9672        7966            52240           26425.0  22981250.0\n",
       "418    ID9841         578            25835           11691.0   9817490.0\n",
       "916    ID9841        2179            25835           11691.0   9817490.0\n",
       "449    ID9980        4256            37459           18727.8  16198010.0\n",
       "680    ID9980        7527            37459           18727.8  16198010.0\n",
       "\n",
       "[74 rows x 5 columns]"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_of_duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        area(sqft)  tot_impulse_itm  tot_icecream_vol    tot_revnew\n",
      "count    74.000000        74.000000         74.000000  7.400000e+01\n",
      "mean   4600.797297     50594.648649      20331.740541  1.639028e+07\n",
      "std    2701.819326     19461.398446       6544.804174  5.595174e+06\n",
      "min     145.000000         0.000000       9466.000000  7.063010e+06\n",
      "25%    2209.500000     38603.000000      15057.200000  1.272428e+07\n",
      "50%    4537.500000     46791.000000      22069.000000  1.759203e+07\n",
      "75%    6597.500000     67224.000000      24981.000000  1.963119e+07\n",
      "max    9831.000000     85510.000000      40760.000000  3.477513e+07\n",
      "Outlet_ID           0\n",
      "area(sqft)          0\n",
      "tot_impulse_itm     2\n",
      "tot_icecream_vol    0\n",
      "tot_revnew          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df_of_duplicates.describe())\n",
    "num_zeros = df_of_duplicates.eq(0).sum()\n",
    "print(num_zeros)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = df_of_duplicates[['Outlet_ID','tot_impulse_itm', 'tot_icecream_vol', 'tot_revnew','area(sqft)']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "predict['area(sqft)'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "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>Outlet_ID</th>\n",
       "      <th>tot_impulse_itm</th>\n",
       "      <th>tot_icecream_vol</th>\n",
       "      <th>tot_revnew</th>\n",
       "      <th>area(sqft)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>ID1365</td>\n",
       "      <td>39285</td>\n",
       "      <td>11171.0</td>\n",
       "      <td>7603540.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>ID1365</td>\n",
       "      <td>39285</td>\n",
       "      <td>11171.0</td>\n",
       "      <td>7603540.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>ID1429</td>\n",
       "      <td>25391</td>\n",
       "      <td>11430.2</td>\n",
       "      <td>9571970.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>ID1429</td>\n",
       "      <td>25391</td>\n",
       "      <td>11430.2</td>\n",
       "      <td>9571970.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>ID1485</td>\n",
       "      <td>44778</td>\n",
       "      <td>24490.6</td>\n",
       "      <td>21663150.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>ID9672</td>\n",
       "      <td>52240</td>\n",
       "      <td>26425.0</td>\n",
       "      <td>22981250.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>ID9841</td>\n",
       "      <td>25835</td>\n",
       "      <td>11691.0</td>\n",
       "      <td>9817490.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>ID9841</td>\n",
       "      <td>25835</td>\n",
       "      <td>11691.0</td>\n",
       "      <td>9817490.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>ID9980</td>\n",
       "      <td>37459</td>\n",
       "      <td>18727.8</td>\n",
       "      <td>16198010.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>ID9980</td>\n",
       "      <td>37459</td>\n",
       "      <td>18727.8</td>\n",
       "      <td>16198010.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Outlet_ID  tot_impulse_itm  tot_icecream_vol  tot_revnew  area(sqft)\n",
       "98     ID1365            39285           11171.0   7603540.0         NaN\n",
       "494    ID1365            39285           11171.0   7603540.0         NaN\n",
       "399    ID1429            25391           11430.2   9571970.0         NaN\n",
       "851    ID1429            25391           11430.2   9571970.0         NaN\n",
       "292    ID1485            44778           24490.6  21663150.0         NaN\n",
       "..        ...              ...               ...         ...         ...\n",
       "576    ID9672            52240           26425.0  22981250.0         NaN\n",
       "418    ID9841            25835           11691.0   9817490.0         NaN\n",
       "916    ID9841            25835           11691.0   9817490.0         NaN\n",
       "449    ID9980            37459           18727.8  16198010.0         NaN\n",
       "680    ID9980            37459           18727.8  16198010.0         NaN\n",
       "\n",
       "[74 rows x 5 columns]"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To split the train dataset into two separate dataframes train_x and train_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outlet_ID              0\n",
      "area(sqft)             0\n",
      "BP1                  235\n",
      "BP2                  235\n",
      "BP3                  235\n",
      "BP4                  235\n",
      "BP5                  235\n",
      "IP1                  230\n",
      "IP2                  230\n",
      "IP3                  230\n",
      "IP4                  230\n",
      "IP5                  230\n",
      "tot_bulk_itm         235\n",
      "tot_impulse_itm      230\n",
      "tot_icecream_vol       0\n",
      "Num_Products_Sold      0\n",
      "tot_revnew             0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "num_zeros = sales_grouped_outlats.eq(0).sum()\n",
    "print(num_zeros)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create bulk_items_data data frame\n",
    "bulk_items_data = sales_grouped_outlats[sales_grouped_outlats['tot_bulk_itm'] != 0]\n",
    "\n",
    "# create impulse_items_data data frame\n",
    "impulse_items_data = sales_grouped_outlats[sales_grouped_outlats['tot_impulse_itm'] != 0]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pandas as pd\n",
    "# from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "# from sklearn.ensemble import RandomForestRegressor\n",
    "# from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "# # Split into features and target\n",
    "# X = bulk_items_data[['tot_bulk_itm', 'tot_icecream_vol', 'tot_revnew']]\n",
    "# y = bulk_items_data['area(sqft)']\n",
    "\n",
    "# # Split into training and testing sets\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# # Define parameter grid for hyperparameter tuning\n",
    "# param_grid = {\n",
    "#     'n_estimators': [100, 200, 300],\n",
    "#     'max_depth': [None, 5, 10, 15],\n",
    "#     'min_samples_split': [2, 5, 10],\n",
    "#     'min_samples_leaf': [1, 2, 4]\n",
    "# }\n",
    "\n",
    "# # Train and evaluate the RandomForestRegressor model with hyperparameter tuning\n",
    "# rf = RandomForestRegressor(random_state=42)\n",
    "# grid_search = GridSearchCV(rf, param_grid, cv=5, n_jobs=-1)\n",
    "# grid_search.fit(X_train, y_train)\n",
    "\n",
    "# # Get the best hyperparameters and evaluate the model on the testing set\n",
    "# best_rf = grid_search.best_estimator_\n",
    "# y_pred = best_rf.predict(X_test)\n",
    "# mse = mean_squared_error(y_test, y_pred)\n",
    "# r2 = r2_score(y_test, y_pred)\n",
    "# print(f'Random Forest:\\nMSE: {mse}\\nR^2: {r2}\\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict.drop_duplicates(subset='Outlet_ID', keep='first', inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Outlet_ID</th>\n",
       "      <th>tot_impulse_itm</th>\n",
       "      <th>tot_icecream_vol</th>\n",
       "      <th>tot_revnew</th>\n",
       "      <th>area(sqft)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>ID1365</td>\n",
       "      <td>39285</td>\n",
       "      <td>11171.0</td>\n",
       "      <td>7603540.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>ID1429</td>\n",
       "      <td>25391</td>\n",
       "      <td>11430.2</td>\n",
       "      <td>9571970.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>ID1485</td>\n",
       "      <td>44778</td>\n",
       "      <td>24490.6</td>\n",
       "      <td>21663150.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>ID1543</td>\n",
       "      <td>42122</td>\n",
       "      <td>21142.4</td>\n",
       "      <td>18341640.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>15156.4</td>\n",
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       "      <td>ID2077</td>\n",
       "      <td>45009</td>\n",
       "      <td>20019.8</td>\n",
       "      <td>16728200.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>ID2170</td>\n",
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       "      <td>10527440.0</td>\n",
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       "      <td>23105.4</td>\n",
       "      <td>17331370.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>ID3549</td>\n",
       "      <td>43385</td>\n",
       "      <td>21041.0</td>\n",
       "      <td>17959500.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>357</th>\n",
       "      <td>ID3741</td>\n",
       "      <td>25371</td>\n",
       "      <td>15057.2</td>\n",
       "      <td>13631640.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>ID3946</td>\n",
       "      <td>82968</td>\n",
       "      <td>26056.6</td>\n",
       "      <td>18810070.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>ID3985</td>\n",
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       "      <td>25796.4</td>\n",
       "      <td>19978090.0</td>\n",
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       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>ID4003</td>\n",
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       "      <td>10233.2</td>\n",
       "      <td>7063010.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>ID4015</td>\n",
       "      <td>85510</td>\n",
       "      <td>40760.0</td>\n",
       "      <td>34775130.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>ID4520</td>\n",
       "      <td>21611</td>\n",
       "      <td>13889.2</td>\n",
       "      <td>12724280.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>235</th>\n",
       "      <td>ID5563</td>\n",
       "      <td>46791</td>\n",
       "      <td>12297.2</td>\n",
       "      <td>7908030.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>25037.8</td>\n",
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       "      <td>ID6214</td>\n",
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       "      <td>22352.0</td>\n",
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       "      <td>ID6574</td>\n",
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       "      <td>22069.0</td>\n",
       "      <td>19631190.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>ID6628</td>\n",
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       "      <td>22480.6</td>\n",
       "      <td>16113850.0</td>\n",
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       "      <td>22231.6</td>\n",
       "      <td>17045900.0</td>\n",
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       "      <td>24981.0</td>\n",
       "      <td>19290810.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>656</th>\n",
       "      <td>ID7358</td>\n",
       "      <td>71489</td>\n",
       "      <td>26210.8</td>\n",
       "      <td>20341880.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>ID7422</td>\n",
       "      <td>44092</td>\n",
       "      <td>11702.4</td>\n",
       "      <td>7621840.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>ID7799</td>\n",
       "      <td>64013</td>\n",
       "      <td>23857.6</td>\n",
       "      <td>18692500.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>ID7948</td>\n",
       "      <td>66336</td>\n",
       "      <td>25028.2</td>\n",
       "      <td>19735490.0</td>\n",
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       "      <th>320</th>\n",
       "      <td>ID8278</td>\n",
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       "      <td>ID8564</td>\n",
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       "      <td>20059.4</td>\n",
       "      <td>17592030.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>ID8631</td>\n",
       "      <td>34647</td>\n",
       "      <td>10495.4</td>\n",
       "      <td>7413370.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>ID9465</td>\n",
       "      <td>48377</td>\n",
       "      <td>21924.4</td>\n",
       "      <td>18376170.0</td>\n",
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       "      <td>25275.0</td>\n",
       "      <td>20369970.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>ID9672</td>\n",
       "      <td>52240</td>\n",
       "      <td>26425.0</td>\n",
       "      <td>22981250.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>ID9841</td>\n",
       "      <td>25835</td>\n",
       "      <td>11691.0</td>\n",
       "      <td>9817490.0</td>\n",
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       "      <th>449</th>\n",
       "      <td>ID9980</td>\n",
       "      <td>37459</td>\n",
       "      <td>18727.8</td>\n",
       "      <td>16198010.0</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "    Outlet_ID  tot_impulse_itm  tot_icecream_vol  tot_revnew  area(sqft)\n",
       "98     ID1365            39285           11171.0   7603540.0         NaN\n",
       "399    ID1429            25391           11430.2   9571970.0         NaN\n",
       "292    ID1485            44778           24490.6  21663150.0         NaN\n",
       "400    ID1543            42122           21142.4  18341640.0         NaN\n",
       "369    ID1750            27082           15156.4  13525100.0         NaN\n",
       "615    ID2074            71200           26438.0  20631800.0         NaN\n",
       "445    ID2077            45009           20019.8  16728200.0         NaN\n",
       "310    ID2170            38603           22870.6  20714290.0         NaN\n",
       "74     ID2819            67224           16864.8  10527440.0         NaN\n",
       "216    ID3127            67432           23105.4  17331370.0         NaN\n",
       "415    ID3549            43385           21041.0  17959500.0         NaN\n",
       "357    ID3741            25371           15057.2  13631640.0         NaN\n",
       "44     ID3946            82968           26056.6  18810070.0         NaN\n",
       "674    ID3985            71627           25796.4  19978090.0         NaN\n",
       "77     ID4003            35006           10233.2   7063010.0         NaN\n",
       "265    ID4015            85510           40760.0  34775130.0         NaN\n",
       "249    ID4520            21611           13889.2  12724280.0         NaN\n",
       "235    ID5563            46791           12297.2   7908030.0         NaN\n",
       "753    ID5913            68529           25037.8  19424170.0         NaN\n",
       "232    ID6214            66045           22352.0  16672100.0         NaN\n",
       "256    ID6574            39635           22069.0  19631190.0         NaN\n",
       "152    ID6628            73958           22480.6  16113850.0         NaN\n",
       "215    ID6708            61823           22231.6  17045900.0         NaN\n",
       "540    ID7029            69315           24981.0  19290810.0         NaN\n",
       "656    ID7358            71489           26210.8  20341880.0         NaN\n",
       "240    ID7422            44092           11702.4   7621840.0         NaN\n",
       "756    ID7799            64013           23857.6  18692500.0         NaN\n",
       "626    ID7948            66336           25028.2  19735490.0         NaN\n",
       "765    ID8229            66202           24439.4  19104500.0         NaN\n",
       "320    ID8278                0            9466.0  10529650.0         NaN\n",
       "394    ID8564            38822           20059.4  17592030.0         NaN\n",
       "25     ID8631            34647           10495.4   7413370.0         NaN\n",
       "441    ID9465            48377           21924.4  18376170.0         NaN\n",
       "601    ID9541            62790           25275.0  20369970.0         NaN\n",
       "359    ID9672            52240           26425.0  22981250.0         NaN\n",
       "418    ID9841            25835           11691.0   9817490.0         NaN\n",
       "449    ID9980            37459           18727.8  16198010.0         NaN"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest:\n",
      "MSE: 2521688.121855449\n",
      "R^2: 0.5399779727658914\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "# Split into features and target\n",
    "X = impulse_items_data[['tot_impulse_itm', 'tot_icecream_vol', 'tot_revnew']]\n",
    "y = impulse_items_data['area(sqft)']\n",
    "\n",
    "# Split into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Define parameter grid for hyperparameter tuning\n",
    "param_grid = {\n",
    "    'n_estimators': [100, 200, 300],\n",
    "    'max_depth': [None, 5, 10, 15],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4]\n",
    "}\n",
    "\n",
    "# Train and evaluate the RandomForestRegressor model with hyperparameter tuning\n",
    "rf = RandomForestRegressor(random_state=42)\n",
    "grid_search = GridSearchCV(rf, param_grid, cv=5, n_jobs=-1)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Get the best hyperparameters and evaluate the model on the testing set\n",
    "best_rf = grid_search.best_estimator_\n",
    "y_pred = best_rf.predict(X_test)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(f'Random Forest:\\nMSE: {mse}\\nR^2: {r2}\\n')\n",
    "\n",
    "# Split into features and target\n",
    "X_pred = predict[['tot_impulse_itm', 'tot_icecream_vol', 'tot_revnew']]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3482.20324576 2557.75236651 6547.49747657 5725.07327184 4637.26376114\n",
      " 5038.25553314 6224.65601784 6229.76734605 3014.23797786 4161.01269969\n",
      " 5921.43895479 4602.86560353 5509.95154941 5333.87605581 3526.351492\n",
      " 6487.03008554 4717.71756223 2830.94043121 5264.10498104 4181.66682182\n",
      " 6153.82138304 5236.67590235 4327.23937994 4848.53971235 5001.97305762\n",
      " 2780.16989001 4656.69774025 5373.52408336 4843.25989934 2552.86420292\n",
      " 6165.99916203 3470.2636916  5633.79812434 5012.10832361 7181.74507474\n",
      " 2645.61412002 6431.20419591]\n"
     ]
    }
   ],
   "source": [
    "# Predict using the trained Random Forest Regressor model\n",
    "y_pred = best_rf.predict(X_pred)\n",
    "\n",
    "# Print the predicted values\n",
    "print(y_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Outlet_ID</th>\n",
       "      <th>area(sqft)</th>\n",
       "      <th>tot_impulse_itm</th>\n",
       "      <th>tot_icecream_vol</th>\n",
       "      <th>tot_revnew</th>\n",
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       "      <th>98</th>\n",
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       "      <th>292</th>\n",
       "      <td>ID1485</td>\n",
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       "      <td>ID9672</td>\n",
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       "      <td>52240</td>\n",
       "      <td>26425.0</td>\n",
       "      <td>22981250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>ID9841</td>\n",
       "      <td>578</td>\n",
       "      <td>25835</td>\n",
       "      <td>11691.0</td>\n",
       "      <td>9817490.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>ID9841</td>\n",
       "      <td>2179</td>\n",
       "      <td>25835</td>\n",
       "      <td>11691.0</td>\n",
       "      <td>9817490.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>ID9980</td>\n",
       "      <td>4256</td>\n",
       "      <td>37459</td>\n",
       "      <td>18727.8</td>\n",
       "      <td>16198010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>ID9980</td>\n",
       "      <td>7527</td>\n",
       "      <td>37459</td>\n",
       "      <td>18727.8</td>\n",
       "      <td>16198010.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Outlet_ID  area(sqft)  tot_impulse_itm  tot_icecream_vol  tot_revnew\n",
       "98     ID1365        5823            39285           11171.0   7603540.0\n",
       "494    ID1365        1291            39285           11171.0   7603540.0\n",
       "399    ID1429        1300            25391           11430.2   9571970.0\n",
       "851    ID1429        2854            25391           11430.2   9571970.0\n",
       "292    ID1485        4535            44778           24490.6  21663150.0\n",
       "..        ...         ...              ...               ...         ...\n",
       "576    ID9672        7966            52240           26425.0  22981250.0\n",
       "418    ID9841         578            25835           11691.0   9817490.0\n",
       "916    ID9841        2179            25835           11691.0   9817490.0\n",
       "449    ID9980        4256            37459           18727.8  16198010.0\n",
       "680    ID9980        7527            37459           18727.8  16198010.0\n",
       "\n",
       "[74 rows x 5 columns]"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_of_duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nadun kumarasinghe\\AppData\\Local\\Temp\\ipykernel_22524\\1710498816.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  predict['area(sqft)'][np.isnan(predict['area(sqft)'])] = y_pred\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Replace null values in predict['area(sqft)'] with predicted values from y_pred\n",
    "predict['area(sqft)'][np.isnan(predict['area(sqft)'])] = y_pred\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>310</th>\n",
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       "      <td>6229.767346</td>\n",
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       "      <td>ID2819</td>\n",
       "      <td>3014.237978</td>\n",
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       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>ID3127</td>\n",
       "      <td>4161.012700</td>\n",
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       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>ID3549</td>\n",
       "      <td>5921.438955</td>\n",
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       "      <th>357</th>\n",
       "      <td>ID3741</td>\n",
       "      <td>4602.865604</td>\n",
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       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>ID3946</td>\n",
       "      <td>5509.951549</td>\n",
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       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>ID3985</td>\n",
       "      <td>5333.876056</td>\n",
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       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>ID4003</td>\n",
       "      <td>3526.351492</td>\n",
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       "      <td>ID4015</td>\n",
       "      <td>6487.030086</td>\n",
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       "      <td>ID4520</td>\n",
       "      <td>4717.717562</td>\n",
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       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>ID5563</td>\n",
       "      <td>2830.940431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>ID5913</td>\n",
       "      <td>5264.104981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>ID6214</td>\n",
       "      <td>4181.666822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>ID6574</td>\n",
       "      <td>6153.821383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>ID6628</td>\n",
       "      <td>5236.675902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>ID6708</td>\n",
       "      <td>4327.239380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>ID7029</td>\n",
       "      <td>4848.539712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>ID7358</td>\n",
       "      <td>5001.973058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>ID7422</td>\n",
       "      <td>2780.169890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>ID7799</td>\n",
       "      <td>4656.697740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>ID7948</td>\n",
       "      <td>5373.524083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>ID8229</td>\n",
       "      <td>4843.259899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>ID8278</td>\n",
       "      <td>2552.864203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>ID8564</td>\n",
       "      <td>6165.999162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ID8631</td>\n",
       "      <td>3470.263692</td>\n",
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       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>ID9465</td>\n",
       "      <td>5633.798124</td>\n",
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       "      <th>601</th>\n",
       "      <td>ID9541</td>\n",
       "      <td>5012.108324</td>\n",
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       "      <th>359</th>\n",
       "      <td>ID9672</td>\n",
       "      <td>7181.745075</td>\n",
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       "      <th>418</th>\n",
       "      <td>ID9841</td>\n",
       "      <td>2645.614120</td>\n",
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       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>ID9980</td>\n",
       "      <td>6431.204196</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "    Outlet_ID   area(sqft)\n",
       "98     ID1365  3482.203246\n",
       "399    ID1429  2557.752367\n",
       "292    ID1485  6547.497477\n",
       "400    ID1543  5725.073272\n",
       "369    ID1750  4637.263761\n",
       "615    ID2074  5038.255533\n",
       "445    ID2077  6224.656018\n",
       "310    ID2170  6229.767346\n",
       "74     ID2819  3014.237978\n",
       "216    ID3127  4161.012700\n",
       "415    ID3549  5921.438955\n",
       "357    ID3741  4602.865604\n",
       "44     ID3946  5509.951549\n",
       "674    ID3985  5333.876056\n",
       "77     ID4003  3526.351492\n",
       "265    ID4015  6487.030086\n",
       "249    ID4520  4717.717562\n",
       "235    ID5563  2830.940431\n",
       "753    ID5913  5264.104981\n",
       "232    ID6214  4181.666822\n",
       "256    ID6574  6153.821383\n",
       "152    ID6628  5236.675902\n",
       "215    ID6708  4327.239380\n",
       "540    ID7029  4848.539712\n",
       "656    ID7358  5001.973058\n",
       "240    ID7422  2780.169890\n",
       "756    ID7799  4656.697740\n",
       "626    ID7948  5373.524083\n",
       "765    ID8229  4843.259899\n",
       "320    ID8278  2552.864203\n",
       "394    ID8564  6165.999162\n",
       "25     ID8631  3470.263692\n",
       "441    ID9465  5633.798124\n",
       "601    ID9541  5012.108324\n",
       "359    ID9672  7181.745075\n",
       "418    ID9841  2645.614120\n",
       "449    ID9980  6431.204196"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict[['Outlet_ID','area(sqft)']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterate through each unique Outlet_ID in df_of_duplicates\n",
    "for outlet_id in df_of_duplicates['Outlet_ID'].unique():\n",
    "    \n",
    "    # Get the predicted area for the current Outlet_ID from predict dataframe\n",
    "    predicted_area = predict.loc[predict['Outlet_ID'] == outlet_id, 'area(sqft)'].values[0]\n",
    "    \n",
    "    # Get the two area values from df_of_duplicates for the current Outlet_ID\n",
    "    area_values = df_of_duplicates.loc[df_of_duplicates['Outlet_ID'] == outlet_id, 'area(sqft)'].values\n",
    "    \n",
    "    # Calculate the absolute difference between the predicted area and the two area values from df_of_duplicates\n",
    "    diff1 = abs(predicted_area - area_values[0])\n",
    "    diff2 = abs(predicted_area - area_values[1])\n",
    "    \n",
    "    # Determine which area value is closer to the predicted area\n",
    "    if diff1 < diff2:\n",
    "        # Drop the row with the second area value\n",
    "        df_of_duplicates = df_of_duplicates.drop(df_of_duplicates.loc[(df_of_duplicates['Outlet_ID'] == outlet_id) & \n",
    "                                                                        (df_of_duplicates['area(sqft)'] == area_values[1])].index)\n",
    "    else:\n",
    "        # Drop the row with the first area value\n",
    "        df_of_duplicates = df_of_duplicates.drop(df_of_duplicates.loc[(df_of_duplicates['Outlet_ID'] == outlet_id) & \n",
    "                                                                        (df_of_duplicates['area(sqft)'] == area_values[0])].index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>494</th>\n",
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       "      <td>11171.0</td>\n",
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       "      <th>851</th>\n",
       "      <td>ID1429</td>\n",
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       "      <td>25391</td>\n",
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       "      <th>292</th>\n",
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       "      <th>593</th>\n",
       "      <td>ID1543</td>\n",
       "      <td>7431</td>\n",
       "      <td>42122</td>\n",
       "      <td>21142.4</td>\n",
       "      <td>18341640.0</td>\n",
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       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>ID1750</td>\n",
       "      <td>1093</td>\n",
       "      <td>27082</td>\n",
       "      <td>15156.4</td>\n",
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       "      <th>615</th>\n",
       "      <td>ID2074</td>\n",
       "      <td>6190</td>\n",
       "      <td>71200</td>\n",
       "      <td>26438.0</td>\n",
       "      <td>20631800.0</td>\n",
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       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>ID2077</td>\n",
       "      <td>4641</td>\n",
       "      <td>45009</td>\n",
       "      <td>20019.8</td>\n",
       "      <td>16728200.0</td>\n",
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       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>ID2170</td>\n",
       "      <td>6169</td>\n",
       "      <td>38603</td>\n",
       "      <td>22870.6</td>\n",
       "      <td>20714290.0</td>\n",
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       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>ID2819</td>\n",
       "      <td>1899</td>\n",
       "      <td>67224</td>\n",
       "      <td>16864.8</td>\n",
       "      <td>10527440.0</td>\n",
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       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>ID3127</td>\n",
       "      <td>4540</td>\n",
       "      <td>67432</td>\n",
       "      <td>23105.4</td>\n",
       "      <td>17331370.0</td>\n",
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       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>ID3549</td>\n",
       "      <td>4292</td>\n",
       "      <td>43385</td>\n",
       "      <td>21041.0</td>\n",
       "      <td>17959500.0</td>\n",
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       "      <th>357</th>\n",
       "      <td>ID3741</td>\n",
       "      <td>6768</td>\n",
       "      <td>25371</td>\n",
       "      <td>15057.2</td>\n",
       "      <td>13631640.0</td>\n",
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       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>ID3946</td>\n",
       "      <td>5044</td>\n",
       "      <td>82968</td>\n",
       "      <td>26056.6</td>\n",
       "      <td>18810070.0</td>\n",
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       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>ID3985</td>\n",
       "      <td>6440</td>\n",
       "      <td>71627</td>\n",
       "      <td>25796.4</td>\n",
       "      <td>19978090.0</td>\n",
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       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>ID4003</td>\n",
       "      <td>2990</td>\n",
       "      <td>35006</td>\n",
       "      <td>10233.2</td>\n",
       "      <td>7063010.0</td>\n",
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       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>ID4015</td>\n",
       "      <td>6027</td>\n",
       "      <td>85510</td>\n",
       "      <td>40760.0</td>\n",
       "      <td>34775130.0</td>\n",
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       "    <tr>\n",
       "      <th>811</th>\n",
       "      <td>ID4520</td>\n",
       "      <td>2141</td>\n",
       "      <td>21611</td>\n",
       "      <td>13889.2</td>\n",
       "      <td>12724280.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>ID5563</td>\n",
       "      <td>2985</td>\n",
       "      <td>46791</td>\n",
       "      <td>12297.2</td>\n",
       "      <td>7908030.0</td>\n",
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       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>ID5913</td>\n",
       "      <td>3673</td>\n",
       "      <td>68529</td>\n",
       "      <td>25037.8</td>\n",
       "      <td>19424170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>ID6214</td>\n",
       "      <td>4052</td>\n",
       "      <td>66045</td>\n",
       "      <td>22352.0</td>\n",
       "      <td>16672100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>ID6574</td>\n",
       "      <td>4980</td>\n",
       "      <td>39635</td>\n",
       "      <td>22069.0</td>\n",
       "      <td>19631190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>ID6628</td>\n",
       "      <td>4727</td>\n",
       "      <td>73958</td>\n",
       "      <td>22480.6</td>\n",
       "      <td>16113850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>ID6708</td>\n",
       "      <td>5992</td>\n",
       "      <td>61823</td>\n",
       "      <td>22231.6</td>\n",
       "      <td>17045900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>ID7029</td>\n",
       "      <td>3616</td>\n",
       "      <td>69315</td>\n",
       "      <td>24981.0</td>\n",
       "      <td>19290810.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>ID7358</td>\n",
       "      <td>5024</td>\n",
       "      <td>71489</td>\n",
       "      <td>26210.8</td>\n",
       "      <td>20341880.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>967</th>\n",
       "      <td>ID7422</td>\n",
       "      <td>2021</td>\n",
       "      <td>44092</td>\n",
       "      <td>11702.4</td>\n",
       "      <td>7621840.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>857</th>\n",
       "      <td>ID7799</td>\n",
       "      <td>1712</td>\n",
       "      <td>64013</td>\n",
       "      <td>23857.6</td>\n",
       "      <td>18692500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>ID7948</td>\n",
       "      <td>2832</td>\n",
       "      <td>66336</td>\n",
       "      <td>25028.2</td>\n",
       "      <td>19735490.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>ID8229</td>\n",
       "      <td>2319</td>\n",
       "      <td>66202</td>\n",
       "      <td>24439.4</td>\n",
       "      <td>19104500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>ID8278</td>\n",
       "      <td>3600</td>\n",
       "      <td>0</td>\n",
       "      <td>9466.0</td>\n",
       "      <td>10529650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>ID8564</td>\n",
       "      <td>6145</td>\n",
       "      <td>38822</td>\n",
       "      <td>20059.4</td>\n",
       "      <td>17592030.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>ID8631</td>\n",
       "      <td>3196</td>\n",
       "      <td>34647</td>\n",
       "      <td>10495.4</td>\n",
       "      <td>7413370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>ID9465</td>\n",
       "      <td>6650</td>\n",
       "      <td>48377</td>\n",
       "      <td>21924.4</td>\n",
       "      <td>18376170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>ID9541</td>\n",
       "      <td>5468</td>\n",
       "      <td>62790</td>\n",
       "      <td>25275.0</td>\n",
       "      <td>20369970.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>ID9672</td>\n",
       "      <td>7165</td>\n",
       "      <td>52240</td>\n",
       "      <td>26425.0</td>\n",
       "      <td>22981250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>ID9841</td>\n",
       "      <td>2179</td>\n",
       "      <td>25835</td>\n",
       "      <td>11691.0</td>\n",
       "      <td>9817490.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>ID9980</td>\n",
       "      <td>7527</td>\n",
       "      <td>37459</td>\n",
       "      <td>18727.8</td>\n",
       "      <td>16198010.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Outlet_ID  area(sqft)  tot_impulse_itm  tot_icecream_vol  tot_revnew\n",
       "494    ID1365        1291            39285           11171.0   7603540.0\n",
       "851    ID1429        2854            25391           11430.2   9571970.0\n",
       "292    ID1485        4535            44778           24490.6  21663150.0\n",
       "593    ID1543        7431            42122           21142.4  18341640.0\n",
       "877    ID1750        1093            27082           15156.4  13525100.0\n",
       "615    ID2074        6190            71200           26438.0  20631800.0\n",
       "445    ID2077        4641            45009           20019.8  16728200.0\n",
       "525    ID2170        6169            38603           22870.6  20714290.0\n",
       "866    ID2819        1899            67224           16864.8  10527440.0\n",
       "698    ID3127        4540            67432           23105.4  17331370.0\n",
       "415    ID3549        4292            43385           21041.0  17959500.0\n",
       "357    ID3741        6768            25371           15057.2  13631640.0\n",
       "44     ID3946        5044            82968           26056.6  18810070.0\n",
       "674    ID3985        6440            71627           25796.4  19978090.0\n",
       "444    ID4003        2990            35006           10233.2   7063010.0\n",
       "731    ID4015        6027            85510           40760.0  34775130.0\n",
       "811    ID4520        2141            21611           13889.2  12724280.0\n",
       "884    ID5563        2985            46791           12297.2   7908030.0\n",
       "872    ID5913        3673            68529           25037.8  19424170.0\n",
       "564    ID6214        4052            66045           22352.0  16672100.0\n",
       "256    ID6574        4980            39635           22069.0  19631190.0\n",
       "152    ID6628        4727            73958           22480.6  16113850.0\n",
       "749    ID6708        5992            61823           22231.6  17045900.0\n",
       "974    ID7029        3616            69315           24981.0  19290810.0\n",
       "656    ID7358        5024            71489           26210.8  20341880.0\n",
       "967    ID7422        2021            44092           11702.4   7621840.0\n",
       "857    ID7799        1712            64013           23857.6  18692500.0\n",
       "829    ID7948        2832            66336           25028.2  19735490.0\n",
       "846    ID8229        2319            66202           24439.4  19104500.0\n",
       "320    ID8278        3600                0            9466.0  10529650.0\n",
       "722    ID8564        6145            38822           20059.4  17592030.0\n",
       "406    ID8631        3196            34647           10495.4   7413370.0\n",
       "563    ID9465        6650            48377           21924.4  18376170.0\n",
       "601    ID9541        5468            62790           25275.0  20369970.0\n",
       "359    ID9672        7165            52240           26425.0  22981250.0\n",
       "916    ID9841        2179            25835           11691.0   9817490.0\n",
       "680    ID9980        7527            37459           18727.8  16198010.0"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_of_duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "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>Outlet_ID</th>\n",
       "      <th>area(sqft)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>ID1365</td>\n",
       "      <td>1291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>ID1429</td>\n",
       "      <td>2854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>ID1485</td>\n",
       "      <td>4535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>ID1543</td>\n",
       "      <td>7431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>ID1750</td>\n",
       "      <td>1093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>ID2074</td>\n",
       "      <td>6190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>ID2077</td>\n",
       "      <td>4641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>ID2170</td>\n",
       "      <td>6169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>ID2819</td>\n",
       "      <td>1899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>ID3127</td>\n",
       "      <td>4540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>ID3549</td>\n",
       "      <td>4292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>ID3741</td>\n",
       "      <td>6768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>ID3946</td>\n",
       "      <td>5044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>ID3985</td>\n",
       "      <td>6440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>ID4003</td>\n",
       "      <td>2990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>ID4015</td>\n",
       "      <td>6027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>811</th>\n",
       "      <td>ID4520</td>\n",
       "      <td>2141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>ID5563</td>\n",
       "      <td>2985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>ID5913</td>\n",
       "      <td>3673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>ID6214</td>\n",
       "      <td>4052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>ID6574</td>\n",
       "      <td>4980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>ID6628</td>\n",
       "      <td>4727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>ID6708</td>\n",
       "      <td>5992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>ID7029</td>\n",
       "      <td>3616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>ID7358</td>\n",
       "      <td>5024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>967</th>\n",
       "      <td>ID7422</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>857</th>\n",
       "      <td>ID7799</td>\n",
       "      <td>1712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>ID7948</td>\n",
       "      <td>2832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>ID8229</td>\n",
       "      <td>2319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>ID8278</td>\n",
       "      <td>3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>ID8564</td>\n",
       "      <td>6145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>ID8631</td>\n",
       "      <td>3196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>ID9465</td>\n",
       "      <td>6650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>ID9541</td>\n",
       "      <td>5468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>ID9672</td>\n",
       "      <td>7165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>ID9841</td>\n",
       "      <td>2179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>ID9980</td>\n",
       "      <td>7527</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Outlet_ID  area(sqft)\n",
       "494    ID1365        1291\n",
       "851    ID1429        2854\n",
       "292    ID1485        4535\n",
       "593    ID1543        7431\n",
       "877    ID1750        1093\n",
       "615    ID2074        6190\n",
       "445    ID2077        4641\n",
       "525    ID2170        6169\n",
       "866    ID2819        1899\n",
       "698    ID3127        4540\n",
       "415    ID3549        4292\n",
       "357    ID3741        6768\n",
       "44     ID3946        5044\n",
       "674    ID3985        6440\n",
       "444    ID4003        2990\n",
       "731    ID4015        6027\n",
       "811    ID4520        2141\n",
       "884    ID5563        2985\n",
       "872    ID5913        3673\n",
       "564    ID6214        4052\n",
       "256    ID6574        4980\n",
       "152    ID6628        4727\n",
       "749    ID6708        5992\n",
       "974    ID7029        3616\n",
       "656    ID7358        5024\n",
       "967    ID7422        2021\n",
       "857    ID7799        1712\n",
       "829    ID7948        2832\n",
       "846    ID8229        2319\n",
       "320    ID8278        3600\n",
       "722    ID8564        6145\n",
       "406    ID8631        3196\n",
       "563    ID9465        6650\n",
       "601    ID9541        5468\n",
       "359    ID9672        7165\n",
       "916    ID9841        2179\n",
       "680    ID9980        7527"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_of_duplicates[['Outlet_ID','area(sqft)']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_of_duplicates.to_csv('duplicates_predicted.csv')"
   ]
  }
 ],
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