feature_engg.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c5210695",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns #visualisation\n",
    "import matplotlib.pyplot as plt #visualisation\n",
    "%matplotlib inline \n",
    "sns.set(color_codes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "136a58e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"final_cleaned.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7c06c06c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier              8322\n",
       "Item_Weight                  8322\n",
       "Item_Fat_Content             8322\n",
       "Item_Visibility              8322\n",
       "Item_Type                    8322\n",
       "Item_MRP                     8322\n",
       "Outlet_Identifier            8322\n",
       "Outlet_Establishment_Year    8322\n",
       "Outlet_Size                  8322\n",
       "Outlet_Location_Type         8322\n",
       "Outlet_Type                  8322\n",
       "Item_Outlet_Sales            8322\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "04bfcc6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier              0\n",
       "Item_Weight                  0\n",
       "Item_Fat_Content             0\n",
       "Item_Visibility              0\n",
       "Item_Type                    0\n",
       "Item_MRP                     0\n",
       "Outlet_Identifier            0\n",
       "Outlet_Establishment_Year    0\n",
       "Outlet_Size                  0\n",
       "Outlet_Location_Type         0\n",
       "Outlet_Type                  0\n",
       "Item_Outlet_Sales            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dc208ec",
   "metadata": {},
   "source": [
    "## Cleaning up columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0167b7e",
   "metadata": {},
   "source": [
    "#### Cleaning up the Item_Fat_Content column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "90ef5686",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-5-333a97c919b5>:6: 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",
      "  df[\"Item_Fat_Content\"][i] = df[\"Item_Fat_Content\"][i].replace(df[\"Item_Fat_Content\"][i], \"R\")\n",
      "<ipython-input-5-333a97c919b5>:4: 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",
      "  df[\"Item_Fat_Content\"][i] = df[\"Item_Fat_Content\"][i].replace(df[\"Item_Fat_Content\"][i], \"L\")\n"
     ]
    }
   ],
   "source": [
    "l = len(df[\"Item_Fat_Content\"])\n",
    "for i in range(l):\n",
    "    if df[\"Item_Fat_Content\"][i] == \"LF\" or df[\"Item_Fat_Content\"][i] == \"low fat\" or df[\"Item_Fat_Content\"][i] == \"Low Fat\":\n",
    "        df[\"Item_Fat_Content\"][i] = df[\"Item_Fat_Content\"][i].replace(df[\"Item_Fat_Content\"][i], \"L\")\n",
    "    elif df[\"Item_Fat_Content\"][i] == \"reg\" or df[\"Item_Fat_Content\"][i] == \"Regular\":\n",
    "        df[\"Item_Fat_Content\"][i] = df[\"Item_Fat_Content\"][i].replace(df[\"Item_Fat_Content\"][i], \"R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e8c28137",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "L    5395\n",
       "R    2927\n",
       "Name: Item_Fat_Content, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Item_Fat_Content\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f1d1a7d8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDB14</td>\n",
       "      <td>20.25</td>\n",
       "      <td>R</td>\n",
       "      <td>0.171939</td>\n",
       "      <td>Canned</td>\n",
       "      <td>92.5120</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>186.4240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FDS52</td>\n",
       "      <td>8.89</td>\n",
       "      <td>L</td>\n",
       "      <td>0.009163</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>101.7016</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>101.2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NCN07</td>\n",
       "      <td>18.50</td>\n",
       "      <td>L</td>\n",
       "      <td>0.056816</td>\n",
       "      <td>Others</td>\n",
       "      <td>132.1284</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>263.6568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDI32</td>\n",
       "      <td>17.70</td>\n",
       "      <td>L</td>\n",
       "      <td>0.291865</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>115.1834</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>345.5502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FDR47</td>\n",
       "      <td>17.85</td>\n",
       "      <td>L</td>\n",
       "      <td>0.070088</td>\n",
       "      <td>Breads</td>\n",
       "      <td>196.5794</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>585.2382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0           FDB14        20.25                R         0.171939   \n",
       "1           FDS52         8.89                L         0.009163   \n",
       "2           NCN07        18.50                L         0.056816   \n",
       "3           FDI32        17.70                L         0.291865   \n",
       "4           FDR47        17.85                L         0.070088   \n",
       "\n",
       "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "0                 Canned   92.5120            OUT010   \n",
       "1           Frozen Foods  101.7016            OUT010   \n",
       "2                 Others  132.1284            OUT010   \n",
       "3  Fruits and Vegetables  115.1834            OUT010   \n",
       "4                 Breads  196.5794            OUT010   \n",
       "\n",
       "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type    Outlet_Type  \\\n",
       "0                       1998       Small               Tier 3  Grocery Store   \n",
       "1                       1998       Small               Tier 3  Grocery Store   \n",
       "2                       1998       Small               Tier 3  Grocery Store   \n",
       "3                       1998       Small               Tier 3  Grocery Store   \n",
       "4                       1998       Small               Tier 3  Grocery Store   \n",
       "\n",
       "   Item_Outlet_Sales  \n",
       "0           186.4240  \n",
       "1           101.2016  \n",
       "2           263.6568  \n",
       "3           345.5502  \n",
       "4           585.2382  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dcb7fc22",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import StrMethodFormatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3b965f19",
   "metadata": {
    "scrolled": true
   },
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDB14</td>\n",
       "      <td>20.250</td>\n",
       "      <td>R</td>\n",
       "      <td>0.171939</td>\n",
       "      <td>Canned</td>\n",
       "      <td>92.5120</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>186.4240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FDS52</td>\n",
       "      <td>8.890</td>\n",
       "      <td>L</td>\n",
       "      <td>0.009163</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>101.7016</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>101.2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NCN07</td>\n",
       "      <td>18.500</td>\n",
       "      <td>L</td>\n",
       "      <td>0.056816</td>\n",
       "      <td>Others</td>\n",
       "      <td>132.1284</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>263.6568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDI32</td>\n",
       "      <td>17.700</td>\n",
       "      <td>L</td>\n",
       "      <td>0.291865</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>115.1834</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>345.5502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FDR47</td>\n",
       "      <td>17.850</td>\n",
       "      <td>L</td>\n",
       "      <td>0.070088</td>\n",
       "      <td>Breads</td>\n",
       "      <td>196.5794</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>585.2382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8317</th>\n",
       "      <td>FDT57</td>\n",
       "      <td>15.200</td>\n",
       "      <td>L</td>\n",
       "      <td>0.018943</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>236.0248</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>6636.6944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8318</th>\n",
       "      <td>FDW02</td>\n",
       "      <td>4.805</td>\n",
       "      <td>R</td>\n",
       "      <td>0.037517</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>124.3704</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>3004.0896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8319</th>\n",
       "      <td>NCO02</td>\n",
       "      <td>11.150</td>\n",
       "      <td>L</td>\n",
       "      <td>0.073013</td>\n",
       "      <td>Others</td>\n",
       "      <td>67.5142</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>2306.9970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8320</th>\n",
       "      <td>FDJ15</td>\n",
       "      <td>11.350</td>\n",
       "      <td>R</td>\n",
       "      <td>0.023210</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>183.3608</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>6064.1064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8321</th>\n",
       "      <td>FDL10</td>\n",
       "      <td>8.395</td>\n",
       "      <td>L</td>\n",
       "      <td>0.039301</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>99.5042</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>1884.8798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8322 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0              FDB14       20.250                R         0.171939   \n",
       "1              FDS52        8.890                L         0.009163   \n",
       "2              NCN07       18.500                L         0.056816   \n",
       "3              FDI32       17.700                L         0.291865   \n",
       "4              FDR47       17.850                L         0.070088   \n",
       "...              ...          ...              ...              ...   \n",
       "8317           FDT57       15.200                L         0.018943   \n",
       "8318           FDW02        4.805                R         0.037517   \n",
       "8319           NCO02       11.150                L         0.073013   \n",
       "8320           FDJ15       11.350                R         0.023210   \n",
       "8321           FDL10        8.395                L         0.039301   \n",
       "\n",
       "                  Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "0                    Canned   92.5120            OUT010   \n",
       "1              Frozen Foods  101.7016            OUT010   \n",
       "2                    Others  132.1284            OUT010   \n",
       "3     Fruits and Vegetables  115.1834            OUT010   \n",
       "4                    Breads  196.5794            OUT010   \n",
       "...                     ...       ...               ...   \n",
       "8317            Snack Foods  236.0248            OUT027   \n",
       "8318                  Dairy  124.3704            OUT027   \n",
       "8319                 Others   67.5142            OUT027   \n",
       "8320                  Dairy  183.3608            OUT027   \n",
       "8321            Snack Foods   99.5042            OUT027   \n",
       "\n",
       "      Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0                          1998       Small               Tier 3   \n",
       "1                          1998       Small               Tier 3   \n",
       "2                          1998       Small               Tier 3   \n",
       "3                          1998       Small               Tier 3   \n",
       "4                          1998       Small               Tier 3   \n",
       "...                         ...         ...                  ...   \n",
       "8317                       1985      Medium               Tier 3   \n",
       "8318                       1985      Medium               Tier 3   \n",
       "8319                       1985      Medium               Tier 3   \n",
       "8320                       1985      Medium               Tier 3   \n",
       "8321                       1985      Medium               Tier 3   \n",
       "\n",
       "            Outlet_Type  Item_Outlet_Sales  \n",
       "0         Grocery Store           186.4240  \n",
       "1         Grocery Store           101.2016  \n",
       "2         Grocery Store           263.6568  \n",
       "3         Grocery Store           345.5502  \n",
       "4         Grocery Store           585.2382  \n",
       "...                 ...                ...  \n",
       "8317  Supermarket Type3          6636.6944  \n",
       "8318  Supermarket Type3          3004.0896  \n",
       "8319  Supermarket Type3          2306.9970  \n",
       "8320  Supermarket Type3          6064.1064  \n",
       "8321  Supermarket Type3          1884.8798  \n",
       "\n",
       "[8322 rows x 12 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dffe5079",
   "metadata": {},
   "source": [
    "### Normalizing stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8a20a5fa",
   "metadata": {
    "scrolled": true
   },
   "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>Item_Weight</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20.250</td>\n",
       "      <td>0.171939</td>\n",
       "      <td>92.5120</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.890</td>\n",
       "      <td>0.009163</td>\n",
       "      <td>101.7016</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.500</td>\n",
       "      <td>0.056816</td>\n",
       "      <td>132.1284</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.700</td>\n",
       "      <td>0.291865</td>\n",
       "      <td>115.1834</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.850</td>\n",
       "      <td>0.070088</td>\n",
       "      <td>196.5794</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8317</th>\n",
       "      <td>15.200</td>\n",
       "      <td>0.018943</td>\n",
       "      <td>236.0248</td>\n",
       "      <td>1985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8318</th>\n",
       "      <td>4.805</td>\n",
       "      <td>0.037517</td>\n",
       "      <td>124.3704</td>\n",
       "      <td>1985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8319</th>\n",
       "      <td>11.150</td>\n",
       "      <td>0.073013</td>\n",
       "      <td>67.5142</td>\n",
       "      <td>1985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8320</th>\n",
       "      <td>11.350</td>\n",
       "      <td>0.023210</td>\n",
       "      <td>183.3608</td>\n",
       "      <td>1985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8321</th>\n",
       "      <td>8.395</td>\n",
       "      <td>0.039301</td>\n",
       "      <td>99.5042</td>\n",
       "      <td>1985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8322 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Item_Weight  Item_Visibility  Item_MRP  Outlet_Establishment_Year\n",
       "0          20.250         0.171939   92.5120                       1998\n",
       "1           8.890         0.009163  101.7016                       1998\n",
       "2          18.500         0.056816  132.1284                       1998\n",
       "3          17.700         0.291865  115.1834                       1998\n",
       "4          17.850         0.070088  196.5794                       1998\n",
       "...           ...              ...       ...                        ...\n",
       "8317       15.200         0.018943  236.0248                       1985\n",
       "8318        4.805         0.037517  124.3704                       1985\n",
       "8319       11.150         0.073013   67.5142                       1985\n",
       "8320       11.350         0.023210  183.3608                       1985\n",
       "8321        8.395         0.039301   99.5042                       1985\n",
       "\n",
       "[8322 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Item_Weight','Item_Visibility','Item_MRP','Outlet_Establishment_Year']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e293dd2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "min_max = MinMaxScaler(feature_range=(0, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bb3af154",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.259857\n",
       "1       0.298863\n",
       "2       0.428010\n",
       "3       0.356086\n",
       "4       0.701573\n",
       "          ...   \n",
       "8317    0.868999\n",
       "8318    0.395081\n",
       "8319    0.153754\n",
       "8320    0.645466\n",
       "8321    0.289536\n",
       "Length: 8322, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Item_MRP = min_max.fit_transform(np.array(df['Item_MRP']).reshape(-1,1)).flatten()\n",
    "Item_MRP = pd.Series(Item_MRP)\n",
    "Item_MRP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e260133e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.934504\n",
       "1       0.258113\n",
       "2       0.830307\n",
       "3       0.782673\n",
       "4       0.791605\n",
       "          ...   \n",
       "8317    0.633820\n",
       "8318    0.014885\n",
       "8319    0.392676\n",
       "8320    0.404585\n",
       "8321    0.228639\n",
       "Length: 8322, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Item_Weight = min_max.fit_transform(np.array(df['Item_Weight']).reshape(-1,1)).flatten()\n",
    "Item_Weight = pd.Series(Item_Weight)\n",
    "Item_Weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0c522b14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.541667\n",
       "1       0.541667\n",
       "2       0.541667\n",
       "3       0.541667\n",
       "4       0.541667\n",
       "          ...   \n",
       "8317    0.000000\n",
       "8318    0.000000\n",
       "8319    0.000000\n",
       "8320    0.000000\n",
       "8321    0.000000\n",
       "Length: 8322, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Outlet_Establishment_Year = min_max.fit_transform(np.array(df['Outlet_Establishment_Year']).reshape(-1,1)).flatten()\n",
    "Outlet_Establishment_Year = pd.Series(Outlet_Establishment_Year)\n",
    "Outlet_Establishment_Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3bd170b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Item_MRP'] = Item_MRP\n",
    "df['Item_Weight'] = Item_Weight\n",
    "df['Outlet_Establishment_Year'] = Outlet_Establishment_Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "39e1388a",
   "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>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDB14</td>\n",
       "      <td>0.934504</td>\n",
       "      <td>R</td>\n",
       "      <td>0.171939</td>\n",
       "      <td>Canned</td>\n",
       "      <td>0.259857</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>186.4240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FDS52</td>\n",
       "      <td>0.258113</td>\n",
       "      <td>L</td>\n",
       "      <td>0.009163</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>0.298863</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>101.2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NCN07</td>\n",
       "      <td>0.830307</td>\n",
       "      <td>L</td>\n",
       "      <td>0.056816</td>\n",
       "      <td>Others</td>\n",
       "      <td>0.428010</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>263.6568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDI32</td>\n",
       "      <td>0.782673</td>\n",
       "      <td>L</td>\n",
       "      <td>0.291865</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>0.356086</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>345.5502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FDR47</td>\n",
       "      <td>0.791605</td>\n",
       "      <td>L</td>\n",
       "      <td>0.070088</td>\n",
       "      <td>Breads</td>\n",
       "      <td>0.701573</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>585.2382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8317</th>\n",
       "      <td>FDT57</td>\n",
       "      <td>0.633820</td>\n",
       "      <td>L</td>\n",
       "      <td>0.018943</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>0.868999</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>6636.6944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8318</th>\n",
       "      <td>FDW02</td>\n",
       "      <td>0.014885</td>\n",
       "      <td>R</td>\n",
       "      <td>0.037517</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>0.395081</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>3004.0896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8319</th>\n",
       "      <td>NCO02</td>\n",
       "      <td>0.392676</td>\n",
       "      <td>L</td>\n",
       "      <td>0.073013</td>\n",
       "      <td>Others</td>\n",
       "      <td>0.153754</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>2306.9970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8320</th>\n",
       "      <td>FDJ15</td>\n",
       "      <td>0.404585</td>\n",
       "      <td>R</td>\n",
       "      <td>0.023210</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>0.645466</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>6064.1064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8321</th>\n",
       "      <td>FDL10</td>\n",
       "      <td>0.228639</td>\n",
       "      <td>L</td>\n",
       "      <td>0.039301</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>0.289536</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>1884.8798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8322 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0              FDB14     0.934504                R         0.171939   \n",
       "1              FDS52     0.258113                L         0.009163   \n",
       "2              NCN07     0.830307                L         0.056816   \n",
       "3              FDI32     0.782673                L         0.291865   \n",
       "4              FDR47     0.791605                L         0.070088   \n",
       "...              ...          ...              ...              ...   \n",
       "8317           FDT57     0.633820                L         0.018943   \n",
       "8318           FDW02     0.014885                R         0.037517   \n",
       "8319           NCO02     0.392676                L         0.073013   \n",
       "8320           FDJ15     0.404585                R         0.023210   \n",
       "8321           FDL10     0.228639                L         0.039301   \n",
       "\n",
       "                  Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "0                    Canned  0.259857            OUT010   \n",
       "1              Frozen Foods  0.298863            OUT010   \n",
       "2                    Others  0.428010            OUT010   \n",
       "3     Fruits and Vegetables  0.356086            OUT010   \n",
       "4                    Breads  0.701573            OUT010   \n",
       "...                     ...       ...               ...   \n",
       "8317            Snack Foods  0.868999            OUT027   \n",
       "8318                  Dairy  0.395081            OUT027   \n",
       "8319                 Others  0.153754            OUT027   \n",
       "8320                  Dairy  0.645466            OUT027   \n",
       "8321            Snack Foods  0.289536            OUT027   \n",
       "\n",
       "      Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0                      0.541667       Small               Tier 3   \n",
       "1                      0.541667       Small               Tier 3   \n",
       "2                      0.541667       Small               Tier 3   \n",
       "3                      0.541667       Small               Tier 3   \n",
       "4                      0.541667       Small               Tier 3   \n",
       "...                         ...         ...                  ...   \n",
       "8317                   0.000000      Medium               Tier 3   \n",
       "8318                   0.000000      Medium               Tier 3   \n",
       "8319                   0.000000      Medium               Tier 3   \n",
       "8320                   0.000000      Medium               Tier 3   \n",
       "8321                   0.000000      Medium               Tier 3   \n",
       "\n",
       "            Outlet_Type  Item_Outlet_Sales  \n",
       "0         Grocery Store           186.4240  \n",
       "1         Grocery Store           101.2016  \n",
       "2         Grocery Store           263.6568  \n",
       "3         Grocery Store           345.5502  \n",
       "4         Grocery Store           585.2382  \n",
       "...                 ...                ...  \n",
       "8317  Supermarket Type3          6636.6944  \n",
       "8318  Supermarket Type3          3004.0896  \n",
       "8319  Supermarket Type3          2306.9970  \n",
       "8320  Supermarket Type3          6064.1064  \n",
       "8321  Supermarket Type3          1884.8798  \n",
       "\n",
       "[8322 rows x 12 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4011be7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0\n",
       "1       1\n",
       "2       1\n",
       "3       1\n",
       "4       1\n",
       "       ..\n",
       "8317    1\n",
       "8318    0\n",
       "8319    1\n",
       "8320    0\n",
       "8321    1\n",
       "Name: L, Length: 8322, dtype: uint8"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Item_Fat_Content = pd.get_dummies(df['Item_Fat_Content']).drop('R', axis = 1)['L']\n",
    "Item_Fat_Content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "53805fd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Item_Fat_Content'] = Item_Fat_Content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "058098f0",
   "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>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDB14</td>\n",
       "      <td>0.934504</td>\n",
       "      <td>0</td>\n",
       "      <td>0.171939</td>\n",
       "      <td>Canned</td>\n",
       "      <td>0.259857</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>186.4240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FDS52</td>\n",
       "      <td>0.258113</td>\n",
       "      <td>1</td>\n",
       "      <td>0.009163</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>0.298863</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>101.2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NCN07</td>\n",
       "      <td>0.830307</td>\n",
       "      <td>1</td>\n",
       "      <td>0.056816</td>\n",
       "      <td>Others</td>\n",
       "      <td>0.428010</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>263.6568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDI32</td>\n",
       "      <td>0.782673</td>\n",
       "      <td>1</td>\n",
       "      <td>0.291865</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>0.356086</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>345.5502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FDR47</td>\n",
       "      <td>0.791605</td>\n",
       "      <td>1</td>\n",
       "      <td>0.070088</td>\n",
       "      <td>Breads</td>\n",
       "      <td>0.701573</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>585.2382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8317</th>\n",
       "      <td>FDT57</td>\n",
       "      <td>0.633820</td>\n",
       "      <td>1</td>\n",
       "      <td>0.018943</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>0.868999</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>6636.6944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8318</th>\n",
       "      <td>FDW02</td>\n",
       "      <td>0.014885</td>\n",
       "      <td>0</td>\n",
       "      <td>0.037517</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>0.395081</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>3004.0896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8319</th>\n",
       "      <td>NCO02</td>\n",
       "      <td>0.392676</td>\n",
       "      <td>1</td>\n",
       "      <td>0.073013</td>\n",
       "      <td>Others</td>\n",
       "      <td>0.153754</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>2306.9970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8320</th>\n",
       "      <td>FDJ15</td>\n",
       "      <td>0.404585</td>\n",
       "      <td>0</td>\n",
       "      <td>0.023210</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>0.645466</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>6064.1064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8321</th>\n",
       "      <td>FDL10</td>\n",
       "      <td>0.228639</td>\n",
       "      <td>1</td>\n",
       "      <td>0.039301</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>0.289536</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>1884.8798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8322 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Item_Identifier  Item_Weight  Item_Fat_Content  Item_Visibility  \\\n",
       "0              FDB14     0.934504                 0         0.171939   \n",
       "1              FDS52     0.258113                 1         0.009163   \n",
       "2              NCN07     0.830307                 1         0.056816   \n",
       "3              FDI32     0.782673                 1         0.291865   \n",
       "4              FDR47     0.791605                 1         0.070088   \n",
       "...              ...          ...               ...              ...   \n",
       "8317           FDT57     0.633820                 1         0.018943   \n",
       "8318           FDW02     0.014885                 0         0.037517   \n",
       "8319           NCO02     0.392676                 1         0.073013   \n",
       "8320           FDJ15     0.404585                 0         0.023210   \n",
       "8321           FDL10     0.228639                 1         0.039301   \n",
       "\n",
       "                  Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "0                    Canned  0.259857            OUT010   \n",
       "1              Frozen Foods  0.298863            OUT010   \n",
       "2                    Others  0.428010            OUT010   \n",
       "3     Fruits and Vegetables  0.356086            OUT010   \n",
       "4                    Breads  0.701573            OUT010   \n",
       "...                     ...       ...               ...   \n",
       "8317            Snack Foods  0.868999            OUT027   \n",
       "8318                  Dairy  0.395081            OUT027   \n",
       "8319                 Others  0.153754            OUT027   \n",
       "8320                  Dairy  0.645466            OUT027   \n",
       "8321            Snack Foods  0.289536            OUT027   \n",
       "\n",
       "      Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0                      0.541667       Small               Tier 3   \n",
       "1                      0.541667       Small               Tier 3   \n",
       "2                      0.541667       Small               Tier 3   \n",
       "3                      0.541667       Small               Tier 3   \n",
       "4                      0.541667       Small               Tier 3   \n",
       "...                         ...         ...                  ...   \n",
       "8317                   0.000000      Medium               Tier 3   \n",
       "8318                   0.000000      Medium               Tier 3   \n",
       "8319                   0.000000      Medium               Tier 3   \n",
       "8320                   0.000000      Medium               Tier 3   \n",
       "8321                   0.000000      Medium               Tier 3   \n",
       "\n",
       "            Outlet_Type  Item_Outlet_Sales  \n",
       "0         Grocery Store           186.4240  \n",
       "1         Grocery Store           101.2016  \n",
       "2         Grocery Store           263.6568  \n",
       "3         Grocery Store           345.5502  \n",
       "4         Grocery Store           585.2382  \n",
       "...                 ...                ...  \n",
       "8317  Supermarket Type3          6636.6944  \n",
       "8318  Supermarket Type3          3004.0896  \n",
       "8319  Supermarket Type3          2306.9970  \n",
       "8320  Supermarket Type3          6064.1064  \n",
       "8321  Supermarket Type3          1884.8798  \n",
       "\n",
       "[8322 rows x 12 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f8a00926",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Using Pearson Correlation\n",
    "plt.figure(figsize=(10,10))\n",
    "cor = df.corr()\n",
    "sns.heatmap(cor, annot=True, cmap=plt.cm.Reds)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "831a00cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_MRP             0.566667\n",
       "Item_Outlet_Sales    1.000000\n",
       "Name: Item_Outlet_Sales, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Correlation with output variable\n",
    "cor_target = abs(cor['Item_Outlet_Sales'])\n",
    "#Selecting highly correlated features\n",
    "relevant_features = cor_target[cor_target>0.25]\n",
    "relevant_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7e115d50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Small     4706\n",
       "Medium    2710\n",
       "High       906\n",
       "Name: Outlet_Size, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Outlet_Size'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e0b1a345",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fruits and Vegetables    1208\n",
       "Snack Foods              1161\n",
       "Household                 891\n",
       "Frozen Foods              836\n",
       "Dairy                     660\n",
       "Canned                    637\n",
       "Baking Goods              632\n",
       "Health and Hygiene        508\n",
       "Soft Drinks               439\n",
       "Meat                      414\n",
       "Breads                    246\n",
       "Hard Drinks               210\n",
       "Others                    168\n",
       "Starchy Foods             141\n",
       "Breakfast                 107\n",
       "Seafood                    64\n",
       "Name: Item_Type, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Item_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ac7e46f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "      <th>8319</th>\n",
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       "      <th>8320</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8321</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8322 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      High  Medium  Small\n",
       "0        0       0      1\n",
       "1        0       0      1\n",
       "2        0       0      1\n",
       "3        0       0      1\n",
       "4        0       0      1\n",
       "...    ...     ...    ...\n",
       "8317     0       1      0\n",
       "8318     0       1      0\n",
       "8319     0       1      0\n",
       "8320     0       1      0\n",
       "8321     0       1      0\n",
       "\n",
       "[8322 rows x 3 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(df['Outlet_Size'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "98eb50a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.join(pd.get_dummies(df['Outlet_Size'])).drop('Outlet_Size', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "429be100",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.join(pd.get_dummies(df['Outlet_Location_Type'])).drop('Outlet_Location_Type', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3fdb93bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.join(pd.get_dummies(df['Outlet_Type'])).drop('Outlet_Type', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "68c9fe96",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.join(pd.get_dummies(df['Outlet_Identifier'])).drop('Outlet_Identifier', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "13fd6cc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.join(pd.get_dummies(df['Item_Type'])).drop('Item_Type', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "bf9cce53",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.join(pd.get_dummies(df['Item_Identifier'])).drop('Item_Identifier', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5222aa3e",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-31-f5e62a763d57>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mcor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcorr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheatmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mannot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     44\u001b[0m             )\n\u001b[1;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/seaborn/matrix.py\u001b[0m in \u001b[0;36mheatmap\u001b[0;34m(data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, linewidths, linecolor, cbar, cbar_kws, cbar_ax, square, xticklabels, yticklabels, mask, ax, **kwargs)\u001b[0m\n\u001b[1;32m    546\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0msquare\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    547\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_aspect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"equal\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 548\u001b[0;31m     \u001b[0mplotter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcbar_ax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    549\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/seaborn/matrix.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, ax, cax, kws)\u001b[0m\n\u001b[1;32m    341\u001b[0m         \u001b[0;31m# Annotate the cells with the formatted values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    342\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mannot\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 343\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_annotate_heatmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmesh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    344\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    345\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/seaborn/matrix.py\u001b[0m in \u001b[0;36m_annotate_heatmap\u001b[0;34m(self, ax, mesh)\u001b[0m\n\u001b[1;32m    248\u001b[0m                                        self.annot_data.flat):\n\u001b[1;32m    249\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mm\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmasked\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m                 \u001b[0mlum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrelative_luminance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    251\u001b[0m                 \u001b[0mtext_color\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\".15\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlum\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m.408\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"w\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m                 \u001b[0mannotation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"{:\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfmt\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/seaborn/utils.py\u001b[0m in \u001b[0;36mrelative_luminance\u001b[0;34m(color)\u001b[0m\n\u001b[1;32m    611\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    612\u001b[0m     \"\"\"\n\u001b[0;32m--> 613\u001b[0;31m     \u001b[0mrgb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorConverter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_rgba_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    614\u001b[0m     \u001b[0mrgb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrgb\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m.03928\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrgb\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m12.92\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrgb\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m.055\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m1.055\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m2.4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    615\u001b[0m     \u001b[0mlum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m.2126\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m.7152\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m.0722\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36mto_rgba_array\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m    314\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    315\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 316\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    317\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    318\u001b[0m         \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36mto_rgba\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m    187\u001b[0m         \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    188\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mrgba\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Suppress exception chaining of cache lookup failure.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 189\u001b[0;31m         \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_to_rgba_no_colorcycle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    190\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    191\u001b[0m             \u001b[0m_colors_full_map\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrgba\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36m_to_rgba_no_colorcycle\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m    274\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0malpha\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    275\u001b[0m         \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 276\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0melem\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0melem\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0melem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    277\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"RGBA values should be within 0-1 range\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#Using Pearson Correlation\n",
    "plt.figure(figsize=(25,25))\n",
    "cor = df.corr()\n",
    "sns.heatmap(cor, annot=True, cmap=plt.cm.Reds)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0756d13f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Correlation with output variable\n",
    "cor_target = abs(cor['Item_Outlet_Sales'])\n",
    "#Selecting highly correlated features\n",
    "relevant_features = cor_target[cor_target>0.25]\n",
    "relevant_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4754b47e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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