bigmart-sales-data-prediction.ipynb

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     "text": [
      "/kaggle/input/bigmart-sales-data/Train.csv\n",
      "/kaggle/input/bigmart-sales-data/Test.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.svm import SVR\n",
    "from lightgbm import LGBMRegressor\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "from warnings import filterwarnings\n",
    "filterwarnings('ignore')"
   ]
  },
  {
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   "id": "6d552068",
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     "text": [
      "Collecting lazypredict\r\n",
      "  Downloading lazypredict-0.2.12-py2.py3-none-any.whl (12 kB)\r\n",
      "Requirement already satisfied: click in /opt/conda/lib/python3.10/site-packages (from lazypredict) (8.1.7)\r\n",
      "Requirement already satisfied: scikit-learn in /opt/conda/lib/python3.10/site-packages (from lazypredict) (1.2.2)\r\n",
      "Requirement already satisfied: pandas in /opt/conda/lib/python3.10/site-packages (from lazypredict) (2.0.3)\r\n",
      "Requirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from lazypredict) (4.66.1)\r\n",
      "Requirement already satisfied: joblib in /opt/conda/lib/python3.10/site-packages (from lazypredict) (1.3.2)\r\n",
      "Requirement already satisfied: lightgbm in /opt/conda/lib/python3.10/site-packages (from lazypredict) (3.3.2)\r\n",
      "Requirement already satisfied: xgboost in /opt/conda/lib/python3.10/site-packages (from lazypredict) (2.0.2)\r\n",
      "Requirement already satisfied: wheel in /opt/conda/lib/python3.10/site-packages (from lightgbm->lazypredict) (0.41.2)\r\n",
      "Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from lightgbm->lazypredict) (1.24.3)\r\n",
      "Requirement already satisfied: scipy in /opt/conda/lib/python3.10/site-packages (from lightgbm->lazypredict) (1.11.4)\r\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from scikit-learn->lazypredict) (3.2.0)\r\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.10/site-packages (from pandas->lazypredict) (2.8.2)\r\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas->lazypredict) (2023.3)\r\n",
      "Requirement already satisfied: tzdata>=2022.1 in /opt/conda/lib/python3.10/site-packages (from pandas->lazypredict) (2023.3)\r\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas->lazypredict) (1.16.0)\r\n",
      "Installing collected packages: lazypredict\r\n",
      "Successfully installed lazypredict-0.2.12\r\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install lazypredict"
   ]
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   "source": [
    "## Data Collection and Processing\n",
    "# Read the data\n",
    "df_train = pd.read_csv('/kaggle/input/bigmart-sales-data/Train.csv')"
   ]
  },
  {
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   "outputs": [
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       "      <th>Item_Identifier</th>\n",
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       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
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       "      <th>Outlet_Type</th>\n",
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       "      <th>0</th>\n",
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       "      <td>FDN15</td>\n",
       "      <td>17.500</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>Meat</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2097.2700</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.200</td>\n",
       "      <td>Regular</td>\n",
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       "      <td>732.3800</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.930</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FDP36</td>\n",
       "      <td>10.395</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>51.4008</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>556.6088</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>FDO10</td>\n",
       "      <td>13.650</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.012741</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>57.6588</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>343.5528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FDP10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.127470</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>107.7622</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>4022.7636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>FDH17</td>\n",
       "      <td>16.200</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.016687</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>96.9726</td>\n",
       "      <td>OUT045</td>\n",
       "      <td>2002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>1076.5986</td>\n",
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       "      <th>9</th>\n",
       "      <td>FDU28</td>\n",
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       "      <td>2007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 2</td>\n",
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       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0           FDA15        9.300          Low Fat         0.016047   \n",
       "1           DRC01        5.920          Regular         0.019278   \n",
       "2           FDN15       17.500          Low Fat         0.016760   \n",
       "3           FDX07       19.200          Regular         0.000000   \n",
       "4           NCD19        8.930          Low Fat         0.000000   \n",
       "5           FDP36       10.395          Regular         0.000000   \n",
       "6           FDO10       13.650          Regular         0.012741   \n",
       "7           FDP10          NaN          Low Fat         0.127470   \n",
       "8           FDH17       16.200          Regular         0.016687   \n",
       "9           FDU28       19.200          Regular         0.094450   \n",
       "\n",
       "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "0                  Dairy  249.8092            OUT049   \n",
       "1            Soft Drinks   48.2692            OUT018   \n",
       "2                   Meat  141.6180            OUT049   \n",
       "3  Fruits and Vegetables  182.0950            OUT010   \n",
       "4              Household   53.8614            OUT013   \n",
       "5           Baking Goods   51.4008            OUT018   \n",
       "6            Snack Foods   57.6588            OUT013   \n",
       "7            Snack Foods  107.7622            OUT027   \n",
       "8           Frozen Foods   96.9726            OUT045   \n",
       "9           Frozen Foods  187.8214            OUT017   \n",
       "\n",
       "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0                       1999      Medium               Tier 1   \n",
       "1                       2009      Medium               Tier 3   \n",
       "2                       1999      Medium               Tier 1   \n",
       "3                       1998         NaN               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "5                       2009      Medium               Tier 3   \n",
       "6                       1987        High               Tier 3   \n",
       "7                       1985      Medium               Tier 3   \n",
       "8                       2002         NaN               Tier 2   \n",
       "9                       2007         NaN               Tier 2   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales  \n",
       "0  Supermarket Type1          3735.1380  \n",
       "1  Supermarket Type2           443.4228  \n",
       "2  Supermarket Type1          2097.2700  \n",
       "3      Grocery Store           732.3800  \n",
       "4  Supermarket Type1           994.7052  \n",
       "5  Supermarket Type2           556.6088  \n",
       "6  Supermarket Type1           343.5528  \n",
       "7  Supermarket Type3          4022.7636  \n",
       "8  Supermarket Type1          1076.5986  \n",
       "9  Supermarket Type1          4710.5350  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See first 10 rows\n",
    "df_train.head(10)"
   ]
  },
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    {
     "name": "stdout",
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     "text": [
      "Number of Rows: 8523\n",
      "Number of Features: 12\n"
     ]
    }
   ],
   "source": [
    "# Print number of rows and columns separately\n",
    "\n",
    "print(\"Number of Rows:\", df_train.shape[0])\n",
    "print(\"Number of Features:\", df_train.shape[1])"
   ]
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8523 entries, 0 to 8522\n",
      "Data columns (total 12 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Item_Identifier            8523 non-null   object \n",
      " 1   Item_Weight                7060 non-null   float64\n",
      " 2   Item_Fat_Content           8523 non-null   object \n",
      " 3   Item_Visibility            8523 non-null   float64\n",
      " 4   Item_Type                  8523 non-null   object \n",
      " 5   Item_MRP                   8523 non-null   float64\n",
      " 6   Outlet_Identifier          8523 non-null   object \n",
      " 7   Outlet_Establishment_Year  8523 non-null   int64  \n",
      " 8   Outlet_Size                6113 non-null   object \n",
      " 9   Outlet_Location_Type       8523 non-null   object \n",
      " 10  Outlet_Type                8523 non-null   object \n",
      " 11  Item_Outlet_Sales          8523 non-null   float64\n",
      "dtypes: float64(4), int64(1), object(7)\n",
      "memory usage: 799.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7fe6ea85",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:27.604312Z",
     "iopub.status.busy": "2024-01-11T12:48:27.603464Z",
     "iopub.status.idle": "2024-01-11T12:48:27.620199Z",
     "shell.execute_reply": "2024-01-11T12:48:27.618935Z"
    },
    "papermill": {
     "duration": 0.040892,
     "end_time": "2024-01-11T12:48:27.622715",
     "exception": false,
     "start_time": "2024-01-11T12:48:27.581823",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier                 0\n",
       "Item_Weight                  1463\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                  2410\n",
       "Outlet_Location_Type            0\n",
       "Outlet_Type                     0\n",
       "Item_Outlet_Sales               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for missing values\n",
    "df_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "be7cb0e0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:27.663209Z",
     "iopub.status.busy": "2024-01-11T12:48:27.662666Z",
     "iopub.status.idle": "2024-01-11T12:48:27.672019Z",
     "shell.execute_reply": "2024-01-11T12:48:27.670863Z"
    },
    "papermill": {
     "duration": 0.032617,
     "end_time": "2024-01-11T12:48:27.674322",
     "exception": false,
     "start_time": "2024-01-11T12:48:27.641705",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.857645184135976"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Handling Missing Values\n",
    "# Calculate Mean of Item_Weight column\n",
    "df_train['Item_Weight'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2e9e2f83",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:27.716799Z",
     "iopub.status.busy": "2024-01-11T12:48:27.715464Z",
     "iopub.status.idle": "2024-01-11T12:48:27.726129Z",
     "shell.execute_reply": "2024-01-11T12:48:27.724634Z"
    },
    "papermill": {
     "duration": 0.034762,
     "end_time": "2024-01-11T12:48:27.729277",
     "exception": false,
     "start_time": "2024-01-11T12:48:27.694515",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Fill missing values in Item_Weight column with mean\n",
    "df_train['Item_Weight'].fillna(df_train['Item_Weight'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9592a806",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:27.772008Z",
     "iopub.status.busy": "2024-01-11T12:48:27.771489Z",
     "iopub.status.idle": "2024-01-11T12:48:27.789437Z",
     "shell.execute_reply": "2024-01-11T12:48:27.788185Z"
    },
    "papermill": {
     "duration": 0.042181,
     "end_time": "2024-01-11T12:48:27.792548",
     "exception": false,
     "start_time": "2024-01-11T12:48:27.750367",
     "status": "completed"
    },
    "tags": []
   },
   "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                  2410\n",
       "Outlet_Location_Type            0\n",
       "Outlet_Type                     0\n",
       "Item_Outlet_Sales               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values\n",
    "df_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7e44ece5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:27.938373Z",
     "iopub.status.busy": "2024-01-11T12:48:27.937356Z",
     "iopub.status.idle": "2024-01-11T12:48:27.948648Z",
     "shell.execute_reply": "2024-01-11T12:48:27.947391Z"
    },
    "papermill": {
     "duration": 0.036988,
     "end_time": "2024-01-11T12:48:27.951246",
     "exception": false,
     "start_time": "2024-01-11T12:48:27.914258",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Outlet_Size\n",
       "Medium    2793\n",
       "Small     2388\n",
       "High       932\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Handling Missing Values\n",
    "df_train['Outlet_Size'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2ae68899",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:27.994287Z",
     "iopub.status.busy": "2024-01-11T12:48:27.993856Z",
     "iopub.status.idle": "2024-01-11T12:48:28.012209Z",
     "shell.execute_reply": "2024-01-11T12:48:28.010850Z"
    },
    "papermill": {
     "duration": 0.043851,
     "end_time": "2024-01-11T12:48:28.015263",
     "exception": false,
     "start_time": "2024-01-11T12:48:27.971412",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Outlet_Type\n",
       "Grocery Store         Small\n",
       "Supermarket Type1     Small\n",
       "Supermarket Type2    Medium\n",
       "Supermarket Type3    Medium\n",
       "Name: Outlet_Size, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate mode of Outlet_Size column for each Outlet_type in Outlet_Size\n",
    "df_train.groupby('Outlet_Type')['Outlet_Size'].agg(pd.Series.mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ca72f6b4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:28.059524Z",
     "iopub.status.busy": "2024-01-11T12:48:28.059113Z",
     "iopub.status.idle": "2024-01-11T12:48:28.076380Z",
     "shell.execute_reply": "2024-01-11T12:48:28.075002Z"
    },
    "papermill": {
     "duration": 0.042189,
     "end_time": "2024-01-11T12:48:28.078978",
     "exception": false,
     "start_time": "2024-01-11T12:48:28.036789",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Fill missing values in Outlet_size column with mode of Outlet_size column for each Outlet_type in Outlet_size\n",
    "df_train.loc[df_train['Outlet_Type']=='Grocery Store','Outlet_Size']='Small'\n",
    "df_train.loc[df_train['Outlet_Type']=='Supermarket Type1','Outlet_Size']='Small'\n",
    "df_train.loc[df_train['Outlet_Type']=='Supermarket Type2','Outlet_Size']='Medium'\n",
    "df_train.loc[df_train['Outlet_Type']=='Supermarket Type3','Outlet_Size']='Medium'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e798eb14",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:28.126176Z",
     "iopub.status.busy": "2024-01-11T12:48:28.125471Z",
     "iopub.status.idle": "2024-01-11T12:48:28.141473Z",
     "shell.execute_reply": "2024-01-11T12:48:28.140048Z"
    },
    "papermill": {
     "duration": 0.04385,
     "end_time": "2024-01-11T12:48:28.144397",
     "exception": false,
     "start_time": "2024-01-11T12:48:28.100547",
     "status": "completed"
    },
    "tags": []
   },
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values\n",
    "df_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "536c6e3e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:28.190942Z",
     "iopub.status.busy": "2024-01-11T12:48:28.190494Z",
     "iopub.status.idle": "2024-01-11T12:48:28.224639Z",
     "shell.execute_reply": "2024-01-11T12:48:28.223718Z"
    },
    "papermill": {
     "duration": 0.061601,
     "end_time": "2024-01-11T12:48:28.227493",
     "exception": false,
     "start_time": "2024-01-11T12:48:28.165892",
     "status": "completed"
    },
    "tags": []
   },
   "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",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8523.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.857645</td>\n",
       "      <td>0.066132</td>\n",
       "      <td>140.992782</td>\n",
       "      <td>1997.831867</td>\n",
       "      <td>2181.288914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.226124</td>\n",
       "      <td>0.051598</td>\n",
       "      <td>62.275067</td>\n",
       "      <td>8.371760</td>\n",
       "      <td>1706.499616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.555000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.290000</td>\n",
       "      <td>1985.000000</td>\n",
       "      <td>33.290000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.310000</td>\n",
       "      <td>0.026989</td>\n",
       "      <td>93.826500</td>\n",
       "      <td>1987.000000</td>\n",
       "      <td>834.247400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.857645</td>\n",
       "      <td>0.053931</td>\n",
       "      <td>143.012800</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>1794.331000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.094585</td>\n",
       "      <td>185.643700</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>3101.296400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>21.350000</td>\n",
       "      <td>0.328391</td>\n",
       "      <td>266.888400</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>13086.964800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Item_Weight  Item_Visibility     Item_MRP  Outlet_Establishment_Year  \\\n",
       "count  8523.000000      8523.000000  8523.000000                8523.000000   \n",
       "mean     12.857645         0.066132   140.992782                1997.831867   \n",
       "std       4.226124         0.051598    62.275067                   8.371760   \n",
       "min       4.555000         0.000000    31.290000                1985.000000   \n",
       "25%       9.310000         0.026989    93.826500                1987.000000   \n",
       "50%      12.857645         0.053931   143.012800                1999.000000   \n",
       "75%      16.000000         0.094585   185.643700                2004.000000   \n",
       "max      21.350000         0.328391   266.888400                2009.000000   \n",
       "\n",
       "       Item_Outlet_Sales  \n",
       "count        8523.000000  \n",
       "mean         2181.288914  \n",
       "std          1706.499616  \n",
       "min            33.290000  \n",
       "25%           834.247400  \n",
       "50%          1794.331000  \n",
       "75%          3101.296400  \n",
       "max         13086.964800  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Data Analysis\n",
    "# See descriptive statistics of numerical columns\n",
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4f63f6d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:28.275290Z",
     "iopub.status.busy": "2024-01-11T12:48:28.274054Z",
     "iopub.status.idle": "2024-01-11T12:48:28.280816Z",
     "shell.execute_reply": "2024-01-11T12:48:28.279600Z"
    },
    "papermill": {
     "duration": 0.033567,
     "end_time": "2024-01-11T12:48:28.283704",
     "exception": false,
     "start_time": "2024-01-11T12:48:28.250137",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "## Data Visulaization of Numerical Features\n",
    "# Applying Seaborn\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c6f6edb5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:28.330015Z",
     "iopub.status.busy": "2024-01-11T12:48:28.329496Z",
     "iopub.status.idle": "2024-01-11T12:48:31.393806Z",
     "shell.execute_reply": "2024-01-11T12:48:31.392408Z"
    },
    "papermill": {
     "duration": 3.091168,
     "end_time": "2024-01-11T12:48:31.397059",
     "exception": false,
     "start_time": "2024-01-11T12:48:28.305891",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot desplot for numerical columns\n",
    "cols = ['Item_Weight', 'Item_Visibility', 'Item_MRP', 'Item_Outlet_Sales']\n",
    "for col in cols:\n",
    "    plt.figure(figsize=(10, 5))\n",
    "    sns.displot(df_train[col])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bbac64ef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:31.449225Z",
     "iopub.status.busy": "2024-01-11T12:48:31.448299Z",
     "iopub.status.idle": "2024-01-11T12:48:31.821365Z",
     "shell.execute_reply": "2024-01-11T12:48:31.820038Z"
    },
    "papermill": {
     "duration": 0.402281,
     "end_time": "2024-01-11T12:48:31.824652",
     "exception": false,
     "start_time": "2024-01-11T12:48:31.422371",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot countplot for Outlet_Establishment_Year column\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.countplot(x='Outlet_Establishment_Year', data=df_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bbe62c71",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:31.876858Z",
     "iopub.status.busy": "2024-01-11T12:48:31.876464Z",
     "iopub.status.idle": "2024-01-11T12:48:32.197575Z",
     "shell.execute_reply": "2024-01-11T12:48:32.196153Z"
    },
    "papermill": {
     "duration": 0.351421,
     "end_time": "2024-01-11T12:48:32.200743",
     "exception": false,
     "start_time": "2024-01-11T12:48:31.849322",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Categorical Features\n",
    "# Plot countplot for Item_Fat_Content column\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.countplot(x=\"Item_Fat_Content\", data=df_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6bd53d6f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:32.252456Z",
     "iopub.status.busy": "2024-01-11T12:48:32.251945Z",
     "iopub.status.idle": "2024-01-11T12:48:32.912903Z",
     "shell.execute_reply": "2024-01-11T12:48:32.908969Z"
    },
    "papermill": {
     "duration": 0.690689,
     "end_time": "2024-01-11T12:48:32.916268",
     "exception": false,
     "start_time": "2024-01-11T12:48:32.225579",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot countplot for Item_Type column\n",
    "plt.figure(figsize=(30, 10))\n",
    "sns.countplot(x=\"Item_Type\", data=df_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5a338dea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:32.974007Z",
     "iopub.status.busy": "2024-01-11T12:48:32.973479Z",
     "iopub.status.idle": "2024-01-11T12:48:33.237712Z",
     "shell.execute_reply": "2024-01-11T12:48:33.236522Z"
    },
    "papermill": {
     "duration": 0.295097,
     "end_time": "2024-01-11T12:48:33.240274",
     "exception": false,
     "start_time": "2024-01-11T12:48:32.945177",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot countplot for Outlet_Size column\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.countplot(x=\"Outlet_Size\", data=df_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ad2fb112",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:33.295054Z",
     "iopub.status.busy": "2024-01-11T12:48:33.294110Z",
     "iopub.status.idle": "2024-01-11T12:48:33.580424Z",
     "shell.execute_reply": "2024-01-11T12:48:33.579296Z"
    },
    "papermill": {
     "duration": 0.317011,
     "end_time": "2024-01-11T12:48:33.583122",
     "exception": false,
     "start_time": "2024-01-11T12:48:33.266111",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ePKgJEybI19dXM2bM0KBBg7R69Wp5e1887cOHDyslJUVxcXEaOXKk9u3bp2nTpsnLy0spKSkVf8IAAAAAIA8LZq+99pqCgoIcr2NjY3X27FktXrxYjz/+uMzmiwt8FotF0dHRvzpOVlaWtmzZovT0dMXHx0uSQkNDlZiYqPXr1ysxMVGSlJ6errp16+rll1+Wj4+PYmNjdfr0ac2dO1fJycny8fGpuJMFAAAAgP/yqEsZLw1lpVq0aKFz584pLy/visfJzMyUxWJRXFycY1tYWJhatGihzMxMp7pOnTo5BbDExETZbDZlZWW5eRYAAAAAcHU8KpiV5csvv1RwcLDq1Knj2LZ9+3ZFR0crMjJS/fv31xdffOH0nuzsbIWGhspkMjltDwsLU3Z2tiQpLy9PP/74o8LCwlxqTCaTow4AAAAAKppHXcr4Szt27FBGRobT/WStW7dWUlKSmjZtqpMnTyo9PV2PPPKIli5dqpiYGEmSzWaTv7+/y3gBAQHavXu3pIsPB5EuXhZ5KR8fH/n5+clqtV5T797eHp95AVQBXl7MNYC7qsrnp6qcB2AUT/kMeWwwO378uEaNGqW2bdtqwIABju0jRoxwqrvzzjvVo0cPvfrqq1qwYMH1brNMZrNJdevWNroNAABwGRaLn9EtAPAAnjIXeGQws9lsGjRokAIDAzVr1izHQz/KUqtWLd1xxx366KOPHNssFouOHz/uUmu1WhUQECBJjhW10pWzUgUFBcrPz3fUuaOkxC6b7crviQMAd3l5mT3m/1CAysZmy1dxcYnRbVwz5gHg2lTkXGCx+F3xipzHBbMLFy5oyJAhys3N1YoVK8q8JPG3hIWFadu2bbLb7U73meXk5Cg8PFzSxUB34403utxLlpOTI7vd7nLv2dUqKqr8Ez0AAFVZcXEJ/38NwGPmAs+4oPK/ioqKNHLkSGVnZ2vhwoUKDg7+zffk5eXp008/VWRkpGNbQkKCrFartm3b5tiWk5OjPXv2KCEhwaluw4YNKiwsdGzLyMiQxWJx3K8GAAAAABXNo1bMJk6cqE2bNik1NVXnzp3TV1995dh3yy23aNeuXVq4cKHuuusuNWzYUCdPntTixYv1008/6ZVXXnHUxsTEKD4+XmlpaRo9erR8fX01ffp0RUREqEuXLo66lJQUrV27Vk8//bT69u2r/fv3Kz09XaNGjeI7zAAAAABcNx4VzLZu3SpJmjJlisu+DRs2qH79+iosLNT06dN19uxZ+fn5KSYmRhMnTlRUVJRT/YwZMzR58mSNHz9eRUVFio+P19ixY+Xt/b9TbtKkidLT0zVlyhQNHjxYQUFBGjFihB599NGKPVEAAAAAuIRHBbONGzf+Zk16evoVjeXv769JkyZp0qRJl61r1aqVVq5ceUVjAgAAAEBF8Kh7zAAAAACgOiKYAQAAAIDBCGYAAAAAYDCCGQAAAAAYjGAGAAAAAAbzqKcy4uqYzSaZzSaj2wAqnZISu0pK7Ea3AQAA4EAwq6TMZpMCA2vJy4tFT+BqFReX6OzZPMIZAADwGASzSspsNsnLy6w5y7bq2Emr0e0AlUbDBgF6om+czGYTwQwAAHgMglkld+ykVd8dO2N0GwAAAACuAdfBAQAAAIDBCGYAAAAAYDCCGQAAAAAYjGAGAAAAAAYjmAEAAACAwQhmAAAAAGAwghkAAAAAGIxgBgAAAAAGI5gBAAAAgMEIZgAAAABgMIIZAAAAABiMYAYAAAAABiOYAQAAAIDBCGYAAAAAYDCCGQAAAAAYjGAGAAAAAAYjmAEAAACAwQhmAAAAAGAwghkAAAAAGIxgBgAAAAAGI5gBAAAAgMEIZgAAAABgMIIZAAAAABiMYAYAAAAABiOYAQAAAIDBCGYAAAAAYDCCGQAAAAAYjGAGAAAAAAYjmAEAAACAwQhmAAAAAGAwghkAAAAAGIxgBgAAAAAGI5gBAAAAgME8Kph9+OGHeuyxx5SQkKDo6GglJSVp1apVstvtTnVvvfWWunbtqsjISN1zzz3atGmTy1i5ublKS0tTmzZtFBMToxEjRujkyZMudTt37lTv3r0VFRWlDh06aP78+S7HAwAAAICK5FHB7PXXX5efn59SU1P12muvKSEhQePGjdOcOXMcNR988IHGjRunbt26acGCBYqOjtawYcP01VdfOY01cuRIbd26VRMmTNC0adOUk5OjQYMGqaioyFFz+PBhpaSkqH79+po3b54GDhyomTNnatGiRdfrlAEAAABA3kY3cKnXXntNQUFBjtexsbE6e/asFi9erMcff1xms1kzZ85U9+7dNXLkSElSu3bttH//fs2ZM0cLFiyQJGVlZWnLli1KT09XfHy8JCk0NFSJiYlav369EhMTJUnp6emqW7euXn75Zfn4+Cg2NlanT5/W3LlzlZycLB8fn+v7AwAAAABQLXnUitmloaxUixYtdO7cOeXl5eno0aP67rvv1K1bN6eaxMREbdu2TQUFBZKkzMxMWSwWxcXFOWrCwsLUokULZWZmOrZlZmaqU6dOTgEsMTFRNptNWVlZ5X16AAAAAFAmjwpmZfnyyy8VHBysOnXqKDs7W9LF1a9LNWvWTIWFhTp69KgkKTs7W6GhoTKZTE51YWFhjjHy8vL0448/KiwszKXGZDI56gAAAACgonnUpYy/tGPHDmVkZGj06NGSJKvVKkmyWCxOdaWvS/fbbDb5+/u7jBcQEKDdu3dLuvhwkLLG8vHxkZ+fn2Msd3l7V2zm9fLy+EwNeLSq8hmqKucBGKGqfH6qynkARvGUz5DHBrPjx49r1KhRatu2rQYMGGB0O1fFbDapbt3aRrcB4DIsFj+jWwBgMOYBAJLnzAUeGcxsNpsGDRqkwMBAzZo1S2bzxRQbEBAg6eJqV/369Z3qL91vsVh0/Phxl3GtVqujpnRFrXTlrFRBQYHy8/Mdde4oKbHLZstz+/1XwsvL7DF/iIDKyGbLV3FxidFtXDPmAsB9zAMApIqdCywWvytekfO4YHbhwgUNGTJEubm5WrFihdMliaX3g2VnZzvdG5adna0aNWqoUaNGjrpt27bJbrc73WeWk5Oj8PBwSVKtWrV04403utxLlpOTI7vd7nLv2dUqKqr8Ez1QlRUXl/A5Bao55gEAkufMBZ5xQeV/FRUVaeTIkcrOztbChQsVHBzstL9Ro0Zq2rSp1q1b57Q9IyNDsbGxjqcrJiQkyGq1atu2bY6anJwc7dmzRwkJCY5tCQkJ2rBhgwoLC53GslgsiomJqYhTBAAAAAAXHrViNnHiRG3atEmpqak6d+6c05dG33LLLfLx8dHw4cP1zDPPqHHjxmrbtq0yMjK0a9cu/eMf/3DUxsTEKD4+XmlpaRo9erR8fX01ffp0RUREqEuXLo66lJQUrV27Vk8//bT69u2r/fv3Kz09XaNGjeI7zAAAAABcN24Hs3feeUe33367QkJCytz//fffa8eOHbr33nuveMytW7dKkqZMmeKyb8OGDQoJCVGPHj2Un5+vBQsWaP78+QoNDdXs2bNdVrhmzJihyZMna/z48SoqKlJ8fLzGjh0rb+//nXKTJk2Unp6uKVOmaPDgwQoKCtKIESP06KOPXnHPAAAAAHCt3A5mY8aM0dSpU381mO3atUtjxoy5qmC2cePGK6rr1auXevXqddkaf39/TZo0SZMmTbpsXatWrbRy5cor7hEAAAAAypvb95jZ7fbL7s/Ly5OXl5e7wwMAAABAtXFVK2Z79+7V3r17Ha937Nih4uJilzqbzably5crNDT02jsEAAAAgCruqoLZJ598otmzZ0uSTCaTVqxYoRUrVpRZa7FY9OKLL157hwAAAABQxV1VMHvwwQd15513ym63q1evXhoxYoTT4+eli4HNz89PjRs3dnrQBgAAAACgbFeVnBo0aKAGDRpIkpYsWaJmzZqpXr16FdIYAAAAAFQXbi9ptWnTpjz7AAAAAIBq65quNfznP/+pVatW6ejRo7LZbC5PajSZTPrkk0+uqUEAAAAAqOrcDmYLFy7USy+9pHr16ikqKkoRERHl2RcAAAAAVBtuB7MlS5aoXbt2mj9/vmrUqFGePQEAAABAteL2F0zbbDZ17dqVUAYAAAAA18jtYBYZGamcnJzy7AUAAAAAqiW3g9mECRP08ccfa+3ateXZDwAAAABUO27fYzZy5EgVFRXpL3/5iyZMmKAbbrhBZrNzzjOZTHrvvfeuuUkAAAAAqMrcDmaBgYEKDAxUkyZNyrMfAAAAAKh23A5mS5cuLc8+AAAAAKDacvseMwAAAABA+XB7xeyLL764orrWrVu7ewgAAAAAqBbcDmbJyckymUy/Wfftt9+6ewgAAAAAqBbcDmZLlixx2VZcXKxjx45p5cqVKikp0dNPP31NzQEAAABAdeB2MGvTps2v7rv//vv10EMPafv27YqNjXX3EAAAAABQLVTIwz/MZrO6d++ut956qyKGBwAAAIAqpcKeymi1WpWbm1tRwwMAAABAleH2pYw//PBDmdttNpt27Nih9PR03X777W43BgAAAADVhdvBrGPHjr/6VEa73a7o6GhNnDjR7cYAAAAAoLpwO5hNmjTJJZiZTCZZLBY1btxYzZs3v+bmAAAAAKA6cDuY3X///eXZBwAAAABUW24Hs0sdPHhQx44dkyQ1bNiQ1TIAAAAAuArXFMw++eQTTZkyxRHKSoWEhCg1NVWdOnW6puYAAAAAoDpwO5ht3rxZI0aM0E033aRRo0apWbNmkqRDhw5p5cqVGj58uObOnauEhIRyaxYAAAAAqiK3g9mrr76qiIgIvfnmm6pVq5Zje6dOndS/f3899NBDmjNnDsEMAAAAAH6D218wvW/fPt17771OoaxUrVq1dN9992nfvn3X1BwAAAAAVAduBzNfX19ZrdZf3W+1WuXr6+vu8AAAAABQbbgdzNq2baslS5YoKyvLZd/XX3+tpUuXKjY29pqaAwAAAIDqwO17zP785z+rT58+euihhxQVFaXQ0FBJUk5Ojnbt2qV69erpmWeeKbdGAQAAAKCqcnvFrFGjRnrvvfeUnJwsq9WqjIwMZWRkyGq1asCAAXr33XcVEhJSnr0CAAAAQJXk9opZUVGRfH19lZaWprS0NJf9586dU1FRkby9y+U7rAEAAACgynJ7xeyFF15Qnz59fnV/3759NWXKFHeHBwAAAIBqw+1g9s9//lNdu3b91f1du3ZVZmamu8MDAAAAQLXhdjA7efKkgoODf3V/gwYNdOLECXeHBwAAAIBqw+1gFhgYqJycnF/df+jQIdWpU8fd4QEAAACg2nA7mLVv317Lly/Xnj17XPZ98803WrlypRISEq6pOQAAAACoDtx+ZOKTTz6pf/7zn+rVq5c6duyo5s2bS5IOHDigTZs2KSgoSE8++WS5NQoAAAAAVZXbwSw4OFirV6/WSy+9pA0bNujjjz+WJNWpU0d33323Ro0addl70Mpy+PBhpaen6+uvv9aBAwcUFham999/36kmOTlZ27dvd3lvRkaGmjVr5nidm5uryZMn65NPPlFhYaHat2+vsWPHqkGDBk7v27lzp1588UV9++23qlevnvr27atBgwbJZDJdVe8AAAAA4K5r+pKxBg0a6MUXX5Tdbtfp06clSUFBQW6HmgMHDmjz5s267bbbVFJSIrvdXmZdq1atNHr0aKdtv/wy65EjR+rgwYOaMGGCfH19NWPGDA0aNEirV692fLfa4cOHlZKSori4OI0cOVL79u3TtGnT5OXlpZSUFLfOAQAAAACuVrl8+7PJZFK9evWueZyOHTuqc+fOkqTU1FTt3r27zDqLxaLo6OhfHScrK0tbtmxRenq64uPjJUmhoaFKTEzU+vXrlZiYKElKT09X3bp19fLLL8vHx0exsbE6ffq05s6dq+TkZPn4+FzzOQEAAADAb3H74R8VwWwun3YyMzNlsVgUFxfn2BYWFqYWLVo4fbdaZmamOnXq5BTAEhMTZbPZlJWVVS69AAAAAMBv8ahgdqW2b9+u6OhoRUZGqn///vriiy+c9mdnZys0NNTlksqwsDBlZ2dLkvLy8vTjjz8qLCzMpcZkMjnqAAAAAKCilculjNdT69atlZSUpKZNm+rkyZNKT0/XI488oqVLlyomJkaSZLPZ5O/v7/LegIAAx+WRubm5ki5eFnkpHx8f+fn5yWq1XlOf3t4Vm3m9vCplpgY8RlX5DFWV8wCMUFU+P1XlPACjeMpnqNIFsxEjRji9vvPOO9WjRw+9+uqrWrBggUFdOTObTapbt7bRbQC4DIvFz+gWABiMeQCA5DlzQaULZr9Uq1Yt3XHHHfroo48c2ywWi44fP+5Sa7VaFRAQIEmOFbXSlbNSBQUFys/Pd9S5o6TELpstz+33XwkvL7PH/CECKiObLV/FxSVGt3HNmAsA9zEPAJAqdi6wWPyueEWu0gezsoSFhWnbtm2y2+1O95nl5OQoPDxc0sVAd+ONN7rcS5aTkyO73e5y79nVKiqq/BM9UJUVF5fwOQWqOeYBAJLnzAWecUHlNcjLy9Onn36qyMhIx7aEhARZrVZt27bNsS0nJ0d79uxRQkKCU92GDRtUWFjo2JaRkSGLxeK4Xw0AAAAAKppHrZjl5+dr8+bNkqRjx47p3LlzWrdunSSpTZs2ys7O1sKFC3XXXXepYcOGOnnypBYvXqyffvpJr7zyimOcmJgYxcfHKy0tTaNHj5avr6+mT5+uiIgIdenSxVGXkpKitWvX6umnn1bfvn21f/9+paena9SoUXyHGQAAAIDrxqOC2alTp/Tkk086bSt9vWTJEt1www0qLCzU9OnTdfbsWfn5+SkmJkYTJ05UVFSU0/tmzJihyZMna/z48SoqKlJ8fLzGjh0rb+//nXKTJk2Unp6uKVOmaPDgwQoKCtKIESP06KOPVvzJAgAAAMB/eVQwCwkJ0b59+y5bk56efkVj+fv7a9KkSZo0adJl61q1aqWVK1decY8AAAAAUN4q/T1mAAAAAFDZEcwAAAAAwGAEMwAAAAAwGMEMAAAAAAxGMAMAAAAAgxHMAAAAAMBgBDMAAAAAMBjBDAAAAAAMRjADAAAAAIMRzAAAAADAYAQzAAAAADAYwQwAAAAADEYwAwAAAACDEcwAAAAAwGAEMwAAAAAwGMEMAAAAAAxGMAMAAAAAgxHMAAAAAMBgBDMAAAAAMBjBDAAAAAAMRjADAAAAAIMRzAAAAADAYAQzAAAAADAYwQwAAAAADEYwAwAAAACDEcwAAAAAwGAEMwAAAAAwGMEMAAAAAAxGMAMAAAAAgxHMAAAAAMBgBDMAAAAAMBjBDAAAAAAMRjADAAAAAIMRzAAAAADAYAQzAAAAADAYwQwAAAAADEYwAwAAAACDEcwAAAAAwGAEMwAAAAAwGMEMAAAAAAxGMAMAAAAAgxHMAAAAAMBgHhXMDh8+rPHjxyspKUm33HKLevToUWbdW2+9pa5duyoyMlL33HOPNm3a5FKTm5urtLQ0tWnTRjExMRoxYoROnjzpUrdz50717t1bUVFR6tChg+bPny+73V7u5wYAAAAAv8ajgtmBAwe0efNmNWnSRM2aNSuz5oMPPtC4cePUrVs3LViwQNHR0Ro2bJi++uorp7qRI0dq69atmjBhgqZNm6acnBwNGjRIRUVFjprDhw8rJSVF9evX17x58zRw4EDNnDlTixYtqsjTBAAAAAAn3kY3cKmOHTuqc+fOkqTU1FTt3r3bpWbmzJnq3r27Ro4cKUlq166d9u/frzlz5mjBggWSpKysLG3ZskXp6emKj4+XJIWGhioxMVHr169XYmKiJCk9PV1169bVyy+/LB8fH8XGxur06dOaO3eukpOT5ePjcx3OGgAAAEB151ErZmbz5ds5evSovvvuO3Xr1s1pe2JiorZt26aCggJJUmZmpiwWi+Li4hw1YWFhatGihTIzMx3bMjMz1alTJ6cAlpiYKJvNpqysrPI4JQAAAAD4TR4VzH5Ldna2pIurX5dq1qyZCgsLdfToUUddaGioTCaTU11YWJhjjLy8PP34448KCwtzqTGZTI46AAAAAKhoHnUp42+xWq2SJIvF4rS99HXpfpvNJn9/f5f3BwQEOC6PzM3NLXMsHx8f+fn5OcZyl7d3xWZeL69KlakBj1NVPkNV5TwAI1SVz09VOQ/AKJ7yGapUwayyMJtNqlu3ttFtALgMi8XP6BYAGIx5AIDkOXNBpQpmAQEBki6udtWvX9+x3WazOe23WCw6fvy4y/utVqujpnRFrXTlrFRBQYHy8/Mdde4oKbHLZstz+/1XwsvL7DF/iIDKyGbLV3FxidFtXDPmAsB9zAMApIqdCywWvytekatUwaz0frDs7Gyne8Oys7NVo0YNNWrUyFG3bds22e12p/vMcnJyFB4eLkmqVauWbrzxRpd7yXJycmS3213uPbtaRUWVf6IHqrLi4hI+p0A1xzwAQPKcucAzLqi8Qo0aNVLTpk21bt06p+0ZGRmKjY11PF0xISFBVqtV27Ztc9Tk5ORoz549SkhIcGxLSEjQhg0bVFhY6DSWxWJRTExMBZ8NAAAAAFzkUStm+fn52rx5syTp2LFjOnfunCOEtWnTRkFBQRo+fLieeeYZNW7cWG3btlVGRoZ27dqlf/zjH45xYmJiFB8fr7S0NI0ePVq+vr6aPn26IiIi1KVLF0ddSkqK1q5dq6efflp9+/bV/v37lZ6erlGjRvEdZgAAAACuG48KZqdOndKTTz7ptK309ZIlS9S2bVv16NFD+fn5WrBggebPn6/Q0FDNnj3bZYVrxowZmjx5ssaPH6+ioiLFx8dr7Nix8vb+3yk3adJE6enpmjJligYPHqygoCCNGDFCjz76aMWfLAAAAAD8l0cFs5CQEO3bt+8363r16qVevXpdtsbf31+TJk3SpEmTLlvXqlUrrVy58qr6BAAAAIDyVKnuMQMAAACAqohgBgAAAAAGI5gBAAAAgMEIZgAAAABgMIIZAAAAABiMYAYAAAAABiOYAQAAAIDBCGYAAAAAYDCCGQAAAAAYjGAGAAAAAAYjmAEAAACAwQhmAAAAAGAwghkAAAAAGIxgBgAAAAAGI5gBAAAAgMEIZgAAAABgMIIZAAAAABiMYAYAAAAABiOYAQAAAIDBCGYAAAAAYDCCGQAAAAAYjGAGAAAAAAYjmAEAAACAwQhmAAAAAGAwghkAAAAAGIxgBgAAAAAGI5gBAAAAgMEIZgAAAABgMIIZAAAAABiMYAYAAAAABiOYAQAAAIDBCGYAAAAAYDCCGQAAAAAYjGAGAAAAAAYjmAEAAACAwQhmAAAAAGAwghkAAAAAGIxgBgAAAAAGI5gBAAAAgMEIZgAAAABgMIIZAAAAABis0gWzt99+WxERES6/pk2b5lT31ltvqWvXroqMjNQ999yjTZs2uYyVm5urtLQ0tWnTRjExMRoxYoROnjx5vU4FAAAAACRJ3kY34K6FCxfK39/f8To4ONjx+w8++EDjxo3T0KFD1a5dO2VkZGjYsGF68803FR0d7agbOXKkDh48qAkTJsjX11czZszQoEGDtHr1anl7V9ofDQAAAIBKptKmj1tvvVVBQUFl7ps5c6a6d++ukSNHSpLatWun/fv3a86cOVqwYIEkKSsrS1u2bFF6erri4+MlSaGhoUpMTNT69euVmJh4Xc4DAAAAACrdpYy/5ejRo/ruu+/UrVs3p+2JiYnatm2bCgoKJEmZmZmyWCyKi4tz1ISFhalFixbKzMy8rj0DAAAAqN4qbTDr0aOHWrRooU6dOmnevHkqLi6WJGVnZ0u6uPp1qWbNmqmwsFBHjx511IWGhspkMjnVhYWFOcYAAAAAgOuh0l3KWL9+fQ0fPly33XabTCaTNm7cqBkzZujEiRMaP368rFarJMlisTi9r/R16X6bzeZ0j1qpgIAA7d69+5r79Pau2Mzr5VVpMzXgEarKZ6iqnAdghKry+akq5wEYxVM+Q5UumLVv317t27d3vI6Pj5evr6/eeOMNDR061MDO/sdsNqlu3dpGtwHgMiwWP6NbAGAw5gEAkufMBZUumJWlW7duWrRokb799lsFBARIuvgo/Pr16ztqbDabJDn2WywWHT9+3GUsq9XqqHFXSYldNlveNY3xW7y8zB7zhwiojGy2fBUXlxjdxjVjLgDcxzwAQKrYucBi8bviFbkqEcwuFRYWJuniPWSlvy99XaNGDTVq1MhRt23bNtntdqf7zHJychQeHn7NfRQVVf6JHqjKiotL+JwC1RzzAADJc+YCz7ig8hplZGTIy8tLt9xyixo1aqSmTZtq3bp1LjWxsbHy8fGRJCUkJMhqtWrbtm2OmpycHO3Zs0cJCQnXtX8AAAAA1VulWzFLSUlR27ZtFRERIUnasGGDVq5cqQEDBjguXRw+fLieeeYZNW7cWG3btlVGRoZ27dqlf/zjH45xYmJiFB8fr7S0NI0ePVq+vr6aPn26IiIi1KVLF0PODQAAAED1VOmCWWhoqFavXq3jx4+rpKRETZs2VVpampKTkx01PXr0UH5+vhYsWKD58+crNDRUs2fPVkxMjNNYM2bM0OTJkzV+/HgVFRUpPj5eY8eOlbd3pfuxAAAAAKjEKl0CGTt27BXV9erVS7169bpsjb+/vyZNmqRJkyaVR2sAAAAA4JYqcY8ZAAAAAFRmBDMAAAAAMBjBDAAAAAAMRjADAAAAAIMRzAAAAADAYAQzAAAAADAYwQwAAAAADEYwAwAAAACDEcwAAAAAwGAEMwAAAAAwGMEMAAAAAAxGMAMAAAAAgxHMAAAAAMBgBDMAAAAAMBjBDAAAAAAMRjADAAAAAIMRzAAAAADAYAQzAAAAADAYwQwAAAAADEYwAwAAAACDEcwAAAAAwGAEMwAAAAAwGMEMAAAAAAxGMAMAAAAAgxHMAAAAAMBgBDMAAAAAMBjBDAAAAAAMRjADAAAAAIMRzAAAAADAYAQzAAAAADAYwQwAAAAADEYwAwAAAACDEcwAAAAAwGAEMwAAAAAwGMEMAAAAAAxGMAMAAAAAgxHMAAAAAMBgBDMAAAAAMBjBDAAAAAAMRjADAAAAAIMRzAAAAADAYAQzAAAAADAYwUzSoUOH9Mgjjyg6OlpxcXGaOnWqCgoKjG4LAAAAQDXhbXQDRrNarRo4cKCaNm2qWbNm6cSJE5oyZYouXLig8ePHG90eAAAAgGqg2gez5cuX6/z585o9e7YCAwMlScXFxZo4caKGDBmi4OBgYxsEAAAAUOVV+0sZMzMzFRsb6whlktStWzeVlJRo69atxjUGAAAAoNqo9sEsOztbYWFhTtssFovq16+v7Oxsg7oCAAAAUJ1U+0sZbTabLBaLy/aAgABZrVa3xjSbTQoKqn2trV2WyXTxf0endFRxcUmFHguoSry8Lv57VECAn+x2g5spB6Vzwe97jpS9pNjYZoBKwmT2klT15oGn7xqqIuYB4Ip5X4e5wGw2XXk/FdNC9WYymeTldeX/Ea5FQJ2a1+U4QFVjNletCwZq1Hb9ByYAl1fV5gGLn7/RLQCVkqfMBZ7RhYEsFotyc3NdtlutVgUEBBjQEQAAAIDqptoHs7CwMJd7yXJzc/XTTz+53HsGAAAAABWh2gezhIQEffbZZ7LZbI5t69atk9lsVlxcnIGdAQAAAKguTHZ7Vbjt1X1Wq1Xdu3dXaGiohgwZ4viC6bvvvpsvmAYAAABwXVT7YCZJhw4d0vPPP6+srCzVrl1bSUlJGjVqlHx8fIxuDQAAAEA1QDADAAAAAINV+3vMAAAAAMBoBDMAAAAAMBjBDAAAAAAMRjADAAAAAIMRzAAAAADAYAQzAAAAADCYt9ENAEaLiIj4zZrJkydrzZo1qlWrlubNm1dhvfz73//W//3f/+mrr75STk6O7rjjjgo9HoCLPGkeWLNmjZYtW6bvvvtO+fn5uummm3TPPfdo0KBBfL8mUIE8aR5Yvny51q9fr3379ik/P1/NmzfX4MGD1blz5wo7JoxHMEO1t2LFCqfXvXv3VnJysnr06OHY1rhxY0VFRclsrthF5p07d2rHjh2KiorSzz//XKHHAvA/njQPWK1WtW/fXoMHD1adOnW0a9cuzZ49W8ePH9fzzz9foccGqjNPmgfmzp2r+Ph49e3bV7Vq1dK6dev0xBNPaMqUKbrvvvsq9NgwDsEM1V50dLTLthtvvNFle1BQULkc78KFC6pZs2aZ+5KTkzVw4EDH7wFcH540Dzz88MNOr9u1a6fz58/r9ddf14QJE+Tl5VUuPQBw5knzwNtvv+10nLi4OB07dkyLFi0imFVhBDPgCiUnJ7tcunDo0CFNmzZN27dvV3Fxsdq0aaOxY8eqcePGjpqIiAg9/fTTslqteuedd5SXl6esrKwyj1HR/wIH4Npcj3mgLIGBgSoqKlJJSQnBDDDY9ZgHygp/LVq00DfffFP+JwSPQTAD3HT06FH16dNHv//97zVlyhSZTCbNnTtXDz/8sNatW+d0L8iSJUt022236W9/+5uKiooM7BpAearIeaCoqEiFhYX65ptv9MYbb6hv376qUaNGRZ4OADdcr78PfPnllwoLCyvv9uFBCGaAm2bPnq2AgAAtXrxYvr6+kqRWrVqpU6dOeuutt9SvXz9HbUBAgGbPni2TyWRUuwAqQEXNA0VFRbr11lsdr++77z6lpaWV/wkAuGbX4+8Da9euVVZWlubMmVOuvcOzcN0U4KatW7eqY8eO8vLyUlFRkYqKimSxWHTLLbdo9+7dTrUJCQmEMqAKqqh5wNvbW6tWrdKbb76pMWPGaNOmTRozZkxFnAKAa1TRfx/Yu3evnn32Wd1///08lbGKY8UMcNOZM2f0xhtv6I033nDZ98vLjerVq3e92gJwHVXkPBAZGSlJuv322xUSEqInnnhC/fv3d2wH4Bkqch44duyYBg0apKioKD333HPX1Cc8H8EMcFNAQIDuuOMOPfTQQy77ateu7fSa1TKgarpe80DLli0lSUeOHCGYAR6mouaB06dPKyUlRfXq1dPs2bO5x7QaIJgBboqNjdWBAwd0yy238JQ0oJq6XvPAl19+KUlq1KhRhR0DgHsqYh44f/68Bg0apMLCQi1ZskR16tQpl3Hh2QhmgJtGjBihnj17KiUlRQ8++KB+97vf6T//+Y+2b9+u22+/3ekLKa/U6dOntX37dsfvz58/r3Xr1kmS7rjjDvn5+ZXrOQC4NhUxD/Tr10933XWXwsLCZDab9fXXX2vRokVq3769oqKiKuAsAFyLipgHhg8frr179+pvf/ubfvjhB/3www+OfWV93xqqBoIZ4KYmTZrorbfe0owZMzRx4kTl5eWpfv36at26tSIiItwa88CBA3ryySedtpW+3rBhg0JCQq65bwDlpyLmgZYtW2rlypX64Ycf5O3trZCQEA0fPrzMy6QAGK8i5oGtW7dKkkaPHu2yb9++fdfULzyXyW63241uAgAAAACqMx6XDwAAAAAGI5gBAAAAgMEIZgAAAABgMIIZAAAAABiMYAYAAAAABiOYAQAAAIDBCGYAAAAAYDCCGQAAAAAYjGAGADDE22+/rYiICH3//fdGt+LxkpOTlZycbHQbAIAK5G10AwAAz3HgwAHNmzdPn3/+uc6cOaPAwEC1bdtWQ4cO1e9//3u3xpw7d66aN2+uzp07l1ufmzdv1q5duzR8+PCrfm9qaqo++ugjZWVllVs/5eHgwYP68MMPdd999ykkJMTodpwkJydr+/btv1k3bNgwt/6bAAAIZgCA/1q/fr2eeuopBQYG6oEHHlBISIiOHTumVatW6aOPPtL06dN11113XfW48+bNU9euXcs9mL355ptVKgQcPHhQs2fPVps2bVyCWXp6ukFdXTR06FD17NnT8frf//63li5dqqFDhyosLMyxPSIiwoj2AKBKIJgBAHTkyBH95S9/UaNGjfTmm28qKCjIsW/AgAHq16+f/vKXv+i9995To0aNDOy0evLx8TH0+HFxcU6vfX19tXTpUv3xj39U27ZtDeoKAKoW7jEDAGjhwoXKz8/X888/7xTKJCkoKEjPPfec8vLytGDBAkkXLwfs2LGjyzizZs1yWjWJiIhQXl6e1qxZo4iICEVERCg1NfWyvWzevFkPPfSQoqOjFRMTo8GDB+vAgQOO/ampqXrzzTcd45f+Km8ffvih7r//fkVFRalt27Z65plndOLECZe6Q4cO6cknn1S7du0UFRWlrl27avr06Y79x44d04QJE9S1a1fHWCNGjHC6t+7tt9/Wk08+KeliEC49p88//1xS2feYnTp1SmlpafrjH/+oyMhI3XPPPVqzZo1Tzffff6+IiAilp6drxYoV6ty5s1q2bKkHHnhAu3btKref1erVqxUREaE9e/a47Js7d65atGjh+NklJyerR48e2r17t/r06aOoqCh17NhRy5Ytc3lvQUGBZs6cqbvuukstW7bUHXfcoalTp6qgoKDcegcAT8GKGQBAmzZtUsOGDXX77beXub9169Zq2LChNm/efFXjTp06VWPHjlVUVJQefPBBSVLjxo1/tf6dd95Ramqq4uPj9cwzzyg/P1/Lli3TQw89pDVr1igkJES9e/fWyZMntXXrVk2dOvWq+rlSb7/9tsaMGaPIyEg99dRTOnXqlJYsWaKdO3fqnXfekcVikSTt3btX/fr1k7e3t3r37q2GDRvqyJEj2rhxo0aNGiXp4mV/WVlZ6t69u2644QYdO3ZMy5Yt04ABA/TBBx/Iz89PrVu3VnJyssvlgc2aNSuzvwsXLig5OVlHjhxRv379FBISonXr1ik1NVU2m00DBw50qn///fd1/vx59e7dWyaTSQsXLtTw4cP1ySefqEaNGtf88+ratauee+45rV27VrfccovTvrVr16pNmzYKDg52bLNarRo8eLC6deum7t2768MPP9SECRNUo0YNxyWTJSUleuyxx/Tll1/qwQcfVLNmzbR//3698cYb+u677/Tqq69ec98A4FHsAIBqzWaz2cPDw+2PPfbYZeuGDh1qDw8Pt+fm5tpHjx5t79Chg0vNzJkz7eHh4U7boqOj7aNHj3apXb16tT08PNx+9OhRu91ut587d85+++2328eOHetU99NPP9n/8Ic/OG2fOHGiy3Gu1OjRo+3R0dG/ur+goMAeGxtr79Gjh/3ChQuO7Zs2bbKHh4fbX3nlFce2fv362WNiYuzHjh1zGqOkpMTx+/z8fJdjZGVl2cPDw+1r1qxxbPvwww/t4eHh9n/9618u9f3797f379/f8fr111+3h4eH2999912nvnv37m2Pjo625+bm2u12u/3o0aP28PBwe5s2bexnz5511H7yySf28PBw+8aNG3/153A5ZfX61FNP2ePj4+3FxcWObd988409PDzcvnr1aqdzCQ8Pty9atMix7eeff7YnJSXZY2Nj7QUFBXa73W5/55137DfffLP9iy++cDr2smXL7OHh4fYvv/zSrd4BwFNxKSMAVHPnz5+XJNWuXfuydaX7S+vL22effSabzabu3bvr9OnTjl9ms1m33Xab47K+irZ7926dOnVKffv2la+vr2P7nXfeqbCwMH366aeSpNOnT+uLL77QAw88oJtuuslpDJPJ5Ph9zZo1Hb8vLCzUmTNn1LhxY1ksljIv/bsSmZmZql+/vnr06OHYVqNGDSUnJysvL09ffPGFU31iYqICAgIcr0tXRo8ePerW8cuSlJSkkydPOv13Wrt2rWrWrKkuXbo41ZauMJby8fFR7969derUKX3zzTeSpHXr1qlZs2YKCwtz+vPQrl07Sbpufx4A4HrhUkYAqOauNHBdaYBz13fffSdJLpfhlapTp06FHPeXfvjhB0lSaGioy76wsDB9+eWXkv4XasLDwy873oULFzRv3jy9/fbbOnHihOx2u2Nfbm6uWz0eO3ZMTZo0kdns/O+rpZc+lp5DqRtvvNHpdWlIs9lsbh2/LHFxcapfv77ee+89xcbGqqSkRO+//746derk8t+uQYMGqlWrltO2pk2bSrp4btHR0Tp8+LAOHTqk2NjYMo936tSpcusdADwBwQwAqjl/f3/Vr19f+/btu2zdvn37FBwcrDp16jitCF2quLjY7T5KA8vUqVNVv359l/1eXl5uj22k559/Xm+//bYGDhyo6Oho+fv7y2QyadSoUU4hrSL92s+uPI/v5eWlu+++WytXrtSECRO0c+dOnTx5Uvfcc49b45WUlCg8PFxjxowpc/8NN9xwLe0CgMchmAEA1KFDB61cuVI7duwo8wEgO3bs0LFjxxyXn1ksljJXW365UnM1Sh/DX69ePf3xj3+8bO2vBcPyUHpZYk5OjstqTU5OjmN/ab/79++/7HgfffSR7r33XqenUf78888uq2VXc04NGzbUvn37VFJS4rRqlp2d7XQO11tSUpIWLVqkjRs3KjMzU0FBQYqPj3epO3nypPLy8pxWzUpXTBs2bCjp4kNi9u7dq9jY2Ar97w0AnoJ7zAAASklJUc2aNfXss8/qzJkzTvvOnj2rZ599Vn5+fvp//+//Sbr4l+bc3Fzt3bvXUXfy5El9/PHHLmPXqlXrii6Za9++verUqaN58+apsLDQZf/p06cdv/fz85NUvpfilWrZsqXq1aun5cuXOz2WffPmzTp06JDuvPNOSRe/RqB169ZavXq1SyC9dCWqrNWqpUuXuqwulp7TlVzemJCQoJ9++kkZGRmObUVFRVq6dKlq1aql1q1b//aJVoCbb75ZERERWrVqldavX6/u3bvL29v134CLioq0YsUKx+uCggKtWLFCQUFBuvXWWyVJ3bp104kTJ7Ry5UqX91+4cEF5eXkVdyIAYABWzAAAatq0qaZMmaI///nPuvvuu9WzZ0+FhITo2LFjWrVqlc6cOaOXX37Z8aj7xMRETZs2TcOGDVNycrIuXLigZcuWKTQ01PHwhlK33nqrtm3bpsWLF6tBgwYKCQnRbbfd5tJDnTp1NGHCBP3lL3/R/fffr8TERAUFBemHH37Q5s2b1apVK40fP94xpiS98MILio+Pl5eXl7p3737F51tYWFjm49YDAgLUr18/PfPMMxozZoz69++v7t27Ox6X37BhQz388MOO+rFjx6pv376677771Lt3b8fP7NNPP9W7774r6eJDQ959913VqVNHzZs311dffaXPPvtMgYGBTsdu0aKFvLy8tGDBAuXm5srHx0ft2rVTvXr1XPrs3bu3VqxYodTUVH3zzTdq2LChPvroI+3cuVNpaWnX7X68stx777168cUXJelXL2Ns0KCBFixYoGPHjqlp06bKyMjQt99+q+eff97x+P6kpCR9+OGHevbZZ/X555+rVatWKi4uVnZ2ttatW6eFCxcqMjLyup0XAFQ0ghkAQNLFFYqwsDDNnz9fq1at0tmzZxUYGKi2bdtqyJAhTg+5qFu3rmbPnq0pU6bo73//u0JCQvTUU0/p8OHDLsEsNTVV48eP14wZM3ThwgXdd999ZQYzSbr77rvVoEEDzZ8/X+np6SooKFBwcLBuv/123X///Y66Ll26KDk5WR988IHee+892e32qw5mr7zyisv2xo0bq1+/frr//vtVs2ZNLViwQNOmTVOtWrXUuXNn/fnPf3Z8h5l0cYVo5cqVeuWVV7Rs2TL9/PPPuummm9StWzdHzV//+leZzWatXbtWP//8s1q1aqXFixc7Vh9L1a9fXxMnTtS8efP017/+VcXFxVqyZEmZwaxmzZpaunSppk2bpjVr1ujcuXMKDQ3V5MmTnX5ORrj77rs1bdo0NWrUSFFRUWXWBAQEaMqUKXrhhRe0cuVK/e53v9P48eMd33UnSWazWXPmzNHrr7+ud999Vx9//LH8/PwUEhKi5OTkMh/OAgCVmcl+ve48BgAAVd7p06fVvn17Pf7443riiSdc9icnJ+vMmTN6//33DegOADwX95gBAIBys2bNGhUXFyspKcnoVgCgUuFSRgBAlXD27NkyHxpSysvLS0FBQdexo8qhoKBAVqv1sjX+/v5OX5Rdlm3btunQoUOaO3euOnfurJCQkPJsEwCqPIIZAKBKGD58uLZv3/6r+xs2bKiNGzdex44qh6ysLA0YMOCyNVdy79qrr76qrKwsxcTEaNy4ceXZIgBUC9xjBgCoEnbv3n3Zx+f7+vrqD3/4w3XsqHKwWq0uD2z5pebNm6tBgwbXqSMAqJ4IZgAAAABgMB7+AQAAAAAGI5gBAAAAgMEIZgAAAABgMIIZAAAAABiMYAYAAAAABiOYAQAAAIDBCGYAAAAAYDCCGQAAAAAY7P8D9zF/90ERYO8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot countplot for Outlet_Location_Type column\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.countplot(x=\"Outlet_Location_Type\", data=df_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cef0510f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:33.636066Z",
     "iopub.status.busy": "2024-01-11T12:48:33.635262Z",
     "iopub.status.idle": "2024-01-11T12:48:33.925291Z",
     "shell.execute_reply": "2024-01-11T12:48:33.924058Z"
    },
    "papermill": {
     "duration": 0.319571,
     "end_time": "2024-01-11T12:48:33.928290",
     "exception": false,
     "start_time": "2024-01-11T12:48:33.608719",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot countplot for Outlet_Type column\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.countplot(x=\"Outlet_Type\", data=df_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "591a145c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:33.981238Z",
     "iopub.status.busy": "2024-01-11T12:48:33.980461Z",
     "iopub.status.idle": "2024-01-11T12:48:33.992359Z",
     "shell.execute_reply": "2024-01-11T12:48:33.991104Z"
    },
    "papermill": {
     "duration": 0.041139,
     "end_time": "2024-01-11T12:48:33.994731",
     "exception": false,
     "start_time": "2024-01-11T12:48:33.953592",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Fat_Content\n",
       "Low Fat    5089\n",
       "Regular    2889\n",
       "LF          316\n",
       "reg         117\n",
       "low fat     112\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data Preprocessing\n",
    "# See categories in Item_Fat_Content column\n",
    "df_train['Item_Fat_Content'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5377578b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:34.048747Z",
     "iopub.status.busy": "2024-01-11T12:48:34.048264Z",
     "iopub.status.idle": "2024-01-11T12:48:34.058886Z",
     "shell.execute_reply": "2024-01-11T12:48:34.057345Z"
    },
    "papermill": {
     "duration": 0.041092,
     "end_time": "2024-01-11T12:48:34.061805",
     "exception": false,
     "start_time": "2024-01-11T12:48:34.020713",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Handle Item_Fat_Content column categories with replacement\n",
    "df_train['Item_Fat_Content'].replace({'LF':'Low Fat','reg':'Regular','low fat':'Low Fat'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dccca476",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:34.120013Z",
     "iopub.status.busy": "2024-01-11T12:48:34.118534Z",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Fat_Content\n",
       "Low Fat    5517\n",
       "Regular    3006\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See categories in Item_Fat_Content column\n",
    "df_train['Item_Fat_Content'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "feea4812",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:34.189689Z",
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   "outputs": [],
   "source": [
    "# Handling Categorical Features\n",
    "# Apply label encoding on categorical columns\n",
    "le = LabelEncoder()\n",
    "\n",
    "df_train['Item_Fat_Content'] = le.fit_transform(df_train['Item_Fat_Content'])\n",
    "\n",
    "df_train['Item_Type'] = le.fit_transform(df_train['Item_Type'])\n",
    "\n",
    "df_train['Outlet_Size'] = le.fit_transform(df_train['Outlet_Size'])\n",
    "\n",
    "df_train['Outlet_Location_Type'] = le.fit_transform(df_train['Outlet_Location_Type'])\n",
    "\n",
    "df_train['Outlet_Type'] = le.fit_transform(df_train['Outlet_Type'])\n",
    "\n",
    "df_train['Item_Identifier'] = le.fit_transform(df_train['Item_Identifier'])\n",
    "\n",
    "df_train['Outlet_Identifier'] = le.fit_transform(df_train['Outlet_Identifier'])"
   ]
  },
  {
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   "execution_count": 29,
   "id": "cc7176e6",
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   "outputs": [
    {
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       "<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>156</td>\n",
       "      <td>9.300000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>4</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>9</td>\n",
       "      <td>1999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>5.920000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>14</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>3</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>662</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>10</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>9</td>\n",
       "      <td>1999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1121</td>\n",
       "      <td>19.200000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>0</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1297</td>\n",
       "      <td>8.930000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>1</td>\n",
       "      <td>1987</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>758</td>\n",
       "      <td>10.395000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>51.4008</td>\n",
       "      <td>3</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>556.6088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>696</td>\n",
       "      <td>13.650000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.012741</td>\n",
       "      <td>13</td>\n",
       "      <td>57.6588</td>\n",
       "      <td>1</td>\n",
       "      <td>1987</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>343.5528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>738</td>\n",
       "      <td>12.857645</td>\n",
       "      <td>0</td>\n",
       "      <td>0.127470</td>\n",
       "      <td>13</td>\n",
       "      <td>107.7622</td>\n",
       "      <td>5</td>\n",
       "      <td>1985</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4022.7636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>440</td>\n",
       "      <td>16.200000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.016687</td>\n",
       "      <td>5</td>\n",
       "      <td>96.9726</td>\n",
       "      <td>7</td>\n",
       "      <td>2002</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1076.5986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>990</td>\n",
       "      <td>19.200000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.094450</td>\n",
       "      <td>5</td>\n",
       "      <td>187.8214</td>\n",
       "      <td>2</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4710.5350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Identifier  Item_Weight  Item_Fat_Content  Item_Visibility  Item_Type  \\\n",
       "0              156     9.300000                 0         0.016047          4   \n",
       "1                8     5.920000                 1         0.019278         14   \n",
       "2              662    17.500000                 0         0.016760         10   \n",
       "3             1121    19.200000                 1         0.000000          6   \n",
       "4             1297     8.930000                 0         0.000000          9   \n",
       "5              758    10.395000                 1         0.000000          0   \n",
       "6              696    13.650000                 1         0.012741         13   \n",
       "7              738    12.857645                 0         0.127470         13   \n",
       "8              440    16.200000                 1         0.016687          5   \n",
       "9              990    19.200000                 1         0.094450          5   \n",
       "\n",
       "   Item_MRP  Outlet_Identifier  Outlet_Establishment_Year  Outlet_Size  \\\n",
       "0  249.8092                  9                       1999            1   \n",
       "1   48.2692                  3                       2009            0   \n",
       "2  141.6180                  9                       1999            1   \n",
       "3  182.0950                  0                       1998            1   \n",
       "4   53.8614                  1                       1987            1   \n",
       "5   51.4008                  3                       2009            0   \n",
       "6   57.6588                  1                       1987            1   \n",
       "7  107.7622                  5                       1985            0   \n",
       "8   96.9726                  7                       2002            1   \n",
       "9  187.8214                  2                       2007            1   \n",
       "\n",
       "   Outlet_Location_Type  Outlet_Type  Item_Outlet_Sales  \n",
       "0                     0            1          3735.1380  \n",
       "1                     2            2           443.4228  \n",
       "2                     0            1          2097.2700  \n",
       "3                     2            0           732.3800  \n",
       "4                     2            1           994.7052  \n",
       "5                     2            2           556.6088  \n",
       "6                     2            1           343.5528  \n",
       "7                     2            3          4022.7636  \n",
       "8                     1            1          1076.5986  \n",
       "9                     1            1          4710.5350  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head(10)"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8523 entries, 0 to 8522\n",
      "Data columns (total 12 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Item_Identifier            8523 non-null   int64  \n",
      " 1   Item_Weight                8523 non-null   float64\n",
      " 2   Item_Fat_Content           8523 non-null   int64  \n",
      " 3   Item_Visibility            8523 non-null   float64\n",
      " 4   Item_Type                  8523 non-null   int64  \n",
      " 5   Item_MRP                   8523 non-null   float64\n",
      " 6   Outlet_Identifier          8523 non-null   int64  \n",
      " 7   Outlet_Establishment_Year  8523 non-null   int64  \n",
      " 8   Outlet_Size                8523 non-null   int64  \n",
      " 9   Outlet_Location_Type       8523 non-null   int64  \n",
      " 10  Outlet_Type                8523 non-null   int64  \n",
      " 11  Item_Outlet_Sales          8523 non-null   float64\n",
      "dtypes: float64(4), int64(8)\n",
      "memory usage: 799.2 KB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
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    {
     "data": {
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       "<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",
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       "      <th>Item_Type</th>\n",
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       "      <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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>156</td>\n",
       "      <td>-0.841872</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.970732</td>\n",
       "      <td>4</td>\n",
       "      <td>1.747454</td>\n",
       "      <td>9</td>\n",
       "      <td>1999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.910601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>-1.641706</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.908111</td>\n",
       "      <td>14</td>\n",
       "      <td>-1.489023</td>\n",
       "      <td>3</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.018440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>662</td>\n",
       "      <td>1.098554</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.956917</td>\n",
       "      <td>10</td>\n",
       "      <td>0.010040</td>\n",
       "      <td>9</td>\n",
       "      <td>1999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.049238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1121</td>\n",
       "      <td>1.500838</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.281758</td>\n",
       "      <td>6</td>\n",
       "      <td>0.660050</td>\n",
       "      <td>0</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.849103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1297</td>\n",
       "      <td>-0.929428</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.281758</td>\n",
       "      <td>9</td>\n",
       "      <td>-1.399220</td>\n",
       "      <td>1</td>\n",
       "      <td>1987</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.695373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>758</td>\n",
       "      <td>-0.582754</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.281758</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.438734</td>\n",
       "      <td>3</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.952110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>696</td>\n",
       "      <td>0.187501</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.034813</td>\n",
       "      <td>13</td>\n",
       "      <td>-1.338238</td>\n",
       "      <td>1</td>\n",
       "      <td>1987</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.076967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>738</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.188838</td>\n",
       "      <td>13</td>\n",
       "      <td>-0.533641</td>\n",
       "      <td>5</td>\n",
       "      <td>1985</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1.079158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>440</td>\n",
       "      <td>0.790926</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.958331</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.706908</td>\n",
       "      <td>7</td>\n",
       "      <td>2002</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.647381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>990</td>\n",
       "      <td>1.500838</td>\n",
       "      <td>1</td>\n",
       "      <td>0.548845</td>\n",
       "      <td>5</td>\n",
       "      <td>0.752008</td>\n",
       "      <td>2</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.482212</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Identifier  Item_Weight  Item_Fat_Content  Item_Visibility  Item_Type  \\\n",
       "0              156    -0.841872                 0        -0.970732          4   \n",
       "1                8    -1.641706                 1        -0.908111         14   \n",
       "2              662     1.098554                 0        -0.956917         10   \n",
       "3             1121     1.500838                 1        -1.281758          6   \n",
       "4             1297    -0.929428                 0        -1.281758          9   \n",
       "5              758    -0.582754                 1        -1.281758          0   \n",
       "6              696     0.187501                 1        -1.034813         13   \n",
       "7              738     0.000000                 0         1.188838         13   \n",
       "8              440     0.790926                 1        -0.958331          5   \n",
       "9              990     1.500838                 1         0.548845          5   \n",
       "\n",
       "   Item_MRP  Outlet_Identifier  Outlet_Establishment_Year  Outlet_Size  \\\n",
       "0  1.747454                  9                       1999            1   \n",
       "1 -1.489023                  3                       2009            0   \n",
       "2  0.010040                  9                       1999            1   \n",
       "3  0.660050                  0                       1998            1   \n",
       "4 -1.399220                  1                       1987            1   \n",
       "5 -1.438734                  3                       2009            0   \n",
       "6 -1.338238                  1                       1987            1   \n",
       "7 -0.533641                  5                       1985            0   \n",
       "8 -0.706908                  7                       2002            1   \n",
       "9  0.752008                  2                       2007            1   \n",
       "\n",
       "   Outlet_Location_Type  Outlet_Type  Item_Outlet_Sales  \n",
       "0                     0            1           0.910601  \n",
       "1                     2            2          -1.018440  \n",
       "2                     0            1          -0.049238  \n",
       "3                     2            0          -0.849103  \n",
       "4                     2            1          -0.695373  \n",
       "5                     2            2          -0.952110  \n",
       "6                     2            1          -1.076967  \n",
       "7                     2            3           1.079158  \n",
       "8                     1            1          -0.647381  \n",
       "9                     1            1           1.482212  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data Scaling using StandardScaler\n",
    "\n",
    "sc = StandardScaler()\n",
    "\n",
    "df_train['Item_Weight'] = sc.fit_transform(df_train['Item_Weight'].values.reshape(-1,1))\n",
    "\n",
    "df_train['Item_Visibility'] = sc.fit_transform(df_train['Item_Visibility'].values.reshape(-1,1))\n",
    "\n",
    "df_train['Item_MRP'] = sc.fit_transform(df_train['Item_MRP'].values.reshape(-1,1))\n",
    "\n",
    "df_train['Item_Outlet_Sales'] = sc.fit_transform(df_train['Item_Outlet_Sales'].values.reshape(-1,1))\n",
    "\n",
    "df_train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bcf3bbe9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:34.521936Z",
     "iopub.status.busy": "2024-01-11T12:48:34.521314Z",
     "iopub.status.idle": "2024-01-11T12:48:34.528614Z",
     "shell.execute_reply": "2024-01-11T12:48:34.527435Z"
    },
    "papermill": {
     "duration": 0.039793,
     "end_time": "2024-01-11T12:48:34.531142",
     "exception": false,
     "start_time": "2024-01-11T12:48:34.491349",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "## Separate features from label\n",
    "# Separate features and target\n",
    "\n",
    "Features = df_train.drop(columns = ['Item_Outlet_Sales'])\n",
    "\n",
    "Target = df_train['Item_Outlet_Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "efdba3bc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:34.587347Z",
     "iopub.status.busy": "2024-01-11T12:48:34.586917Z",
     "iopub.status.idle": "2024-01-11T12:48:34.609798Z",
     "shell.execute_reply": "2024-01-11T12:48:34.608365Z"
    },
    "papermill": {
     "duration": 0.05477,
     "end_time": "2024-01-11T12:48:34.612581",
     "exception": false,
     "start_time": "2024-01-11T12:48:34.557811",
     "status": "completed"
    },
    "tags": []
   },
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>156</td>\n",
       "      <td>-0.841872</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.970732</td>\n",
       "      <td>4</td>\n",
       "      <td>1.747454</td>\n",
       "      <td>9</td>\n",
       "      <td>1999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>-1.641706</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.908111</td>\n",
       "      <td>14</td>\n",
       "      <td>-1.489023</td>\n",
       "      <td>3</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>662</td>\n",
       "      <td>1.098554</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.956917</td>\n",
       "      <td>10</td>\n",
       "      <td>0.010040</td>\n",
       "      <td>9</td>\n",
       "      <td>1999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1121</td>\n",
       "      <td>1.500838</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.281758</td>\n",
       "      <td>6</td>\n",
       "      <td>0.660050</td>\n",
       "      <td>0</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1297</td>\n",
       "      <td>-0.929428</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.281758</td>\n",
       "      <td>9</td>\n",
       "      <td>-1.399220</td>\n",
       "      <td>1</td>\n",
       "      <td>1987</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8518</th>\n",
       "      <td>370</td>\n",
       "      <td>-1.418084</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.181193</td>\n",
       "      <td>13</td>\n",
       "      <td>1.180783</td>\n",
       "      <td>1</td>\n",
       "      <td>1987</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8519</th>\n",
       "      <td>897</td>\n",
       "      <td>-1.059578</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.371154</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.527301</td>\n",
       "      <td>7</td>\n",
       "      <td>2002</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8520</th>\n",
       "      <td>1357</td>\n",
       "      <td>-0.534243</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.599784</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.897208</td>\n",
       "      <td>6</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8521</th>\n",
       "      <td>681</td>\n",
       "      <td>-1.336444</td>\n",
       "      <td>1</td>\n",
       "      <td>1.532880</td>\n",
       "      <td>13</td>\n",
       "      <td>-0.607977</td>\n",
       "      <td>3</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8522</th>\n",
       "      <td>50</td>\n",
       "      <td>0.459634</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.411936</td>\n",
       "      <td>14</td>\n",
       "      <td>-1.052261</td>\n",
       "      <td>8</td>\n",
       "      <td>1997</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8523 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Item_Identifier  Item_Weight  Item_Fat_Content  Item_Visibility  \\\n",
       "0                 156    -0.841872                 0        -0.970732   \n",
       "1                   8    -1.641706                 1        -0.908111   \n",
       "2                 662     1.098554                 0        -0.956917   \n",
       "3                1121     1.500838                 1        -1.281758   \n",
       "4                1297    -0.929428                 0        -1.281758   \n",
       "...               ...          ...               ...              ...   \n",
       "8518              370    -1.418084                 0        -0.181193   \n",
       "8519              897    -1.059578                 1        -0.371154   \n",
       "8520             1357    -0.534243                 0        -0.599784   \n",
       "8521              681    -1.336444                 1         1.532880   \n",
       "8522               50     0.459634                 0        -0.411936   \n",
       "\n",
       "      Item_Type  Item_MRP  Outlet_Identifier  Outlet_Establishment_Year  \\\n",
       "0             4  1.747454                  9                       1999   \n",
       "1            14 -1.489023                  3                       2009   \n",
       "2            10  0.010040                  9                       1999   \n",
       "3             6  0.660050                  0                       1998   \n",
       "4             9 -1.399220                  1                       1987   \n",
       "...         ...       ...                ...                        ...   \n",
       "8518         13  1.180783                  1                       1987   \n",
       "8519          0 -0.527301                  7                       2002   \n",
       "8520          8 -0.897208                  6                       2004   \n",
       "8521         13 -0.607977                  3                       2009   \n",
       "8522         14 -1.052261                  8                       1997   \n",
       "\n",
       "      Outlet_Size  Outlet_Location_Type  Outlet_Type  \n",
       "0               1                     0            1  \n",
       "1               0                     2            2  \n",
       "2               1                     0            1  \n",
       "3               1                     2            0  \n",
       "4               1                     2            1  \n",
       "...           ...                   ...          ...  \n",
       "8518            1                     2            1  \n",
       "8519            1                     1            1  \n",
       "8520            1                     1            1  \n",
       "8521            0                     2            2  \n",
       "8522            1                     0            1  \n",
       "\n",
       "[8523 rows x 11 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "43e11cf8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:34.669797Z",
     "iopub.status.busy": "2024-01-11T12:48:34.669380Z",
     "iopub.status.idle": "2024-01-11T12:48:34.678841Z",
     "shell.execute_reply": "2024-01-11T12:48:34.677563Z"
    },
    "papermill": {
     "duration": 0.041052,
     "end_time": "2024-01-11T12:48:34.681423",
     "exception": false,
     "start_time": "2024-01-11T12:48:34.640371",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.910601\n",
       "1      -1.018440\n",
       "2      -0.049238\n",
       "3      -0.849103\n",
       "4      -0.695373\n",
       "          ...   \n",
       "8518    0.349915\n",
       "8519   -0.956402\n",
       "8520   -0.579100\n",
       "8521   -0.196725\n",
       "8522   -0.829594\n",
       "Name: Item_Outlet_Sales, Length: 8523, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "12741801",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:34.738332Z",
     "iopub.status.busy": "2024-01-11T12:48:34.737914Z",
     "iopub.status.idle": "2024-01-11T12:48:34.748257Z",
     "shell.execute_reply": "2024-01-11T12:48:34.747004Z"
    },
    "papermill": {
     "duration": 0.042459,
     "end_time": "2024-01-11T12:48:34.751181",
     "exception": false,
     "start_time": "2024-01-11T12:48:34.708722",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Split data into train and test sets\n",
    "Features_train,Features_test,Target_train,Target_test = train_test_split(Features,Target,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5137da2e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:34.813555Z",
     "iopub.status.busy": "2024-01-11T12:48:34.812950Z",
     "iopub.status.idle": "2024-01-11T12:48:34.820255Z",
     "shell.execute_reply": "2024-01-11T12:48:34.818877Z"
    },
    "papermill": {
     "duration": 0.043001,
     "end_time": "2024-01-11T12:48:34.822897",
     "exception": false,
     "start_time": "2024-01-11T12:48:34.779896",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Features: (8523, 11)\n",
      "Shape of Features_train: (6818, 11)\n",
      "Shape of Features_test: (1705, 11)\n"
     ]
    }
   ],
   "source": [
    "# Print shape of features and training and testing data of features\n",
    "print(\"Shape of Features:\", Features.shape)\n",
    "print(\"Shape of Features_train:\", Features_train.shape)\n",
    "print(\"Shape of Features_test:\", Features_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e0caef37",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:34.883152Z",
     "iopub.status.busy": "2024-01-11T12:48:34.882709Z",
     "iopub.status.idle": "2024-01-11T12:48:34.890017Z",
     "shell.execute_reply": "2024-01-11T12:48:34.888588Z"
    },
    "papermill": {
     "duration": 0.040664,
     "end_time": "2024-01-11T12:48:34.892829",
     "exception": false,
     "start_time": "2024-01-11T12:48:34.852165",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Target: (8523,)\n",
      "Shape of Target_train: (6818,)\n",
      "Shape of Target_test: (1705,)\n"
     ]
    }
   ],
   "source": [
    "# Print shape of Target and training and testing data of target\n",
    "print(\"Shape of Target:\", Target.shape)\n",
    "print(\"Shape of Target_train:\", Target_train.shape)\n",
    "print(\"Shape of Target_test:\", Target_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "07b3c5d4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:34.954761Z",
     "iopub.status.busy": "2024-01-11T12:48:34.953663Z",
     "iopub.status.idle": "2024-01-11T12:48:35.205726Z",
     "shell.execute_reply": "2024-01-11T12:48:35.204846Z"
    },
    "papermill": {
     "duration": 0.286848,
     "end_time": "2024-01-11T12:48:35.208493",
     "exception": false,
     "start_time": "2024-01-11T12:48:34.921645",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Squared: 0.5119126417237851\n",
      "Mean Absolute Error: 0.47173654608004656\n",
      "Mean Squared Error: 0.45559693922553346\n"
     ]
    }
   ],
   "source": [
    "## Building model with XGBRegressor\n",
    "XGBRModel = XGBRegressor()\n",
    "\n",
    "# Fit the model\n",
    "XGBRModel.fit(Features_train,Target_train)\n",
    "\n",
    "# Predict the model\n",
    "Target_pred = XGBRModel.predict(Features_test)\n",
    "\n",
    "# Calculate R-Squared\n",
    "r2_score = metrics.r2_score(Target_test,Target_pred)\n",
    "print(\"R-Squared:\", r2_score)\n",
    "\n",
    "# Calculate Mean Absolute Error\n",
    "mae = metrics.mean_absolute_error(Target_test,Target_pred)\n",
    "print(\"Mean Absolute Error:\", mae)\n",
    "\n",
    "# Calculate Mean Squared Error\n",
    "mse = metrics.mean_squared_error(Target_test,Target_pred)\n",
    "print(\"Mean Squared Error:\", mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9d5b5168",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:35.269795Z",
     "iopub.status.busy": "2024-01-11T12:48:35.269365Z",
     "iopub.status.idle": "2024-01-11T12:48:38.387746Z",
     "shell.execute_reply": "2024-01-11T12:48:38.386182Z"
    },
    "papermill": {
     "duration": 3.152435,
     "end_time": "2024-01-11T12:48:38.390631",
     "exception": false,
     "start_time": "2024-01-11T12:48:35.238196",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Squared: -0.024447002815127128\n",
      "Mean Absolute Error: 0.7432809334031226\n",
      "Mean Squared Error: 0.9562528325456281\n"
     ]
    }
   ],
   "source": [
    "## Building SVR Model\n",
    "# Build model with SVLRegressor\n",
    "model = SVR()\n",
    "\n",
    "# Fitting the model\n",
    "model.fit(Features_train,Target_train)\n",
    "\n",
    "# Predict the model\n",
    "Target_pred = model.predict(Features_test)\n",
    "\n",
    "# Calculate R-Squared\n",
    "r2_score = metrics.r2_score(Target_test,Target_pred)\n",
    "print(\"R-Squared:\", r2_score)\n",
    "\n",
    "# Calculate Mean Absolute Error\n",
    "mae = metrics.mean_absolute_error(Target_test,Target_pred)\n",
    "print(\"Mean Absolute Error:\", mae)\n",
    "\n",
    "# Calculate Mean Squared Error\n",
    "mse = metrics.mean_squared_error(Target_test,Target_pred)\n",
    "print(\"Mean Squared Error:\", mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "444da82e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:38.452685Z",
     "iopub.status.busy": "2024-01-11T12:48:38.451892Z",
     "iopub.status.idle": "2024-01-11T12:48:42.305363Z",
     "shell.execute_reply": "2024-01-11T12:48:42.303425Z"
    },
    "papermill": {
     "duration": 3.889105,
     "end_time": "2024-01-11T12:48:42.309631",
     "exception": false,
     "start_time": "2024-01-11T12:48:38.420526",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Squared: 0.5529212765655414\n",
      "Mean Absolute Error: 0.4480802021803598\n",
      "Mean Squared Error: 0.4173181184388075\n"
     ]
    }
   ],
   "source": [
    "# Building model with RandomForestRegressor\n",
    "model = RandomForestRegressor()\n",
    "\n",
    "# Fitting the model\n",
    "model.fit(Features_train,Target_train)\n",
    "\n",
    "# Predict the model\n",
    "Target_pred = model.predict(Features_test)\n",
    "\n",
    "# Calculate R-Squared\n",
    "r2_score = metrics.r2_score(Target_test,Target_pred)\n",
    "print(\"R-Squared:\", r2_score)\n",
    "\n",
    "# Calculate Mean Absolute Error\n",
    "mae = metrics.mean_absolute_error(Target_test,Target_pred)\n",
    "print(\"Mean Absolute Error:\", mae)\n",
    "\n",
    "# Calculate Mean Squared Error\n",
    "mse = metrics.mean_squared_error(Target_test,Target_pred)\n",
    "print(\"Mean Squared Error:\", mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a16edd20",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:48:42.369362Z",
     "iopub.status.busy": "2024-01-11T12:48:42.367612Z",
     "iopub.status.idle": "2024-01-11T12:49:29.431090Z",
     "shell.execute_reply": "2024-01-11T12:49:29.429118Z"
    },
    "papermill": {
     "duration": 47.095318,
     "end_time": "2024-01-11T12:49:29.433555",
     "exception": false,
     "start_time": "2024-01-11T12:48:42.338237",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 21%|██▏       | 9/42 [00:03<00:20,  1.59it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GammaRegressor model failed to execute\n",
      "Some value(s) of y are out of the valid range of the loss 'HalfGammaLoss'.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 71%|███████▏  | 30/42 [00:37<00:13,  1.10s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PoissonRegressor model failed to execute\n",
      "Some value(s) of y are out of the valid range of the loss 'HalfPoissonLoss'.\n",
      "QuantileRegressor model failed to execute\n",
      "Solver interior-point is not anymore available in SciPy >= 1.11.0.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 42/42 [00:45<00:00,  1.09s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               Adjusted R-Squared  R-Squared  RMSE  Time Taken\n",
      "Model                                                                         \n",
      "NuSVR                                        0.61       0.61  0.60        2.69\n",
      "SVR                                          0.61       0.61  0.60        2.90\n",
      "MLPRegressor                                 0.61       0.61  0.61        7.74\n",
      "GradientBoostingRegressor                    0.60       0.60  0.61        1.22\n",
      "LGBMRegressor                                0.59       0.59  0.62        0.98\n",
      "HistGradientBoostingRegressor                0.58       0.59  0.62        0.55\n",
      "Lars                                         0.58       0.58  0.63        0.04\n",
      "LarsCV                                       0.58       0.58  0.63        0.08\n",
      "LassoLarsIC                                  0.58       0.58  0.63        0.10\n",
      "TransformedTargetRegressor                   0.58       0.58  0.63        0.05\n",
      "LinearRegression                             0.58       0.58  0.63        0.12\n",
      "RidgeCV                                      0.58       0.58  0.63        0.11\n",
      "Ridge                                        0.58       0.58  0.63        0.02\n",
      "KernelRidge                                  0.58       0.58  0.63        5.03\n",
      "BayesianRidge                                0.58       0.58  0.63        0.11\n",
      "ElasticNetCV                                 0.58       0.58  0.63        0.35\n",
      "LassoLarsCV                                  0.58       0.58  0.63        0.08\n",
      "LassoCV                                      0.58       0.58  0.63        0.35\n",
      "OrthogonalMatchingPursuitCV                  0.58       0.58  0.63        0.06\n",
      "SGDRegressor                                 0.58       0.58  0.63        0.10\n",
      "HuberRegressor                               0.57       0.58  0.63        0.07\n",
      "LinearSVR                                    0.57       0.57  0.63        0.21\n",
      "RandomForestRegressor                        0.55       0.56  0.64        3.92\n",
      "ExtraTreesRegressor                          0.55       0.55  0.65        2.05\n",
      "BaggingRegressor                             0.52       0.52  0.67        0.40\n",
      "XGBRegressor                                 0.51       0.51  0.67        0.31\n",
      "RANSACRegressor                              0.50       0.51  0.68        0.40\n",
      "KNeighborsRegressor                          0.50       0.50  0.68        0.16\n",
      "AdaBoostRegressor                            0.40       0.41  0.74        0.45\n",
      "TweedieRegressor                             0.39       0.39  0.75        0.11\n",
      "OrthogonalMatchingPursuit                    0.33       0.33  0.79        0.02\n",
      "DecisionTreeRegressor                        0.20       0.20  0.86        0.15\n",
      "PassiveAggressiveRegressor                   0.08       0.09  0.92        0.04\n",
      "ExtraTreeRegressor                           0.07       0.07  0.93        0.11\n",
      "ElasticNet                                   0.04       0.05  0.94        0.06\n",
      "Lasso                                       -0.01      -0.00  0.97        0.09\n",
      "DummyRegressor                              -0.01      -0.00  0.97        0.02\n",
      "LassoLars                                   -0.01      -0.00  0.97        0.08\n",
      "GaussianProcessRegressor                   -61.37     -60.97  7.61       14.50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Building Lazy Predict Model\n",
    "from lazypredict.Supervised import LazyRegressor\n",
    "\n",
    "reg = LazyRegressor(verbose=0,ignore_warnings=False, custom_metric=None)\n",
    "models,predictions = reg.fit(Features_train,Features_test,Target_train,Target_test)\n",
    "\n",
    "# Print model performance\n",
    "print (models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6d291f94",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:49:29.500398Z",
     "iopub.status.busy": "2024-01-11T12:49:29.499819Z",
     "iopub.status.idle": "2024-01-11T12:49:30.703968Z",
     "shell.execute_reply": "2024-01-11T12:49:30.702542Z"
    },
    "papermill": {
     "duration": 1.241202,
     "end_time": "2024-01-11T12:49:30.707116",
     "exception": false,
     "start_time": "2024-01-11T12:49:29.465914",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Squared: 0.6037397755518279\n",
      "Mean Absolute Error: 0.42452516683004043\n",
      "Mean Squared Error: 0.3698824448824243\n"
     ]
    }
   ],
   "source": [
    "# Building model with GradientBoostingRegressor\n",
    "model = GradientBoostingRegressor()\n",
    "\n",
    "# Fitting the model\n",
    "model.fit(Features_train,Target_train)\n",
    "\n",
    "#Predicting the model\n",
    "Target_pred = model.predict(Features_test)\n",
    "\n",
    "# Calculate R-Squared\n",
    "r2_score = metrics.r2_score(Target_test,Target_pred)\n",
    "print(\"R-Squared:\", r2_score)\n",
    "\n",
    "# Calculate Mean Absolute Error\n",
    "mae = metrics.mean_absolute_error(Target_test,Target_pred)\n",
    "print(\"Mean Absolute Error:\", mae)\n",
    "\n",
    "# Calculate Mean Squared Error\n",
    "mse = metrics.mean_squared_error(Target_test,Target_pred)\n",
    "print(\"Mean Squared Error:\", mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "23b5363a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:49:30.776146Z",
     "iopub.status.busy": "2024-01-11T12:49:30.775705Z",
     "iopub.status.idle": "2024-01-11T12:49:31.792500Z",
     "shell.execute_reply": "2024-01-11T12:49:31.790671Z"
    },
    "papermill": {
     "duration": 1.054457,
     "end_time": "2024-01-11T12:49:31.795577",
     "exception": false,
     "start_time": "2024-01-11T12:49:30.741120",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Squared: 0.5865680743618109\n",
      "Mean Absolute Error: 0.43394911824178806\n",
      "Mean Squared Error: 0.3859110806805262\n"
     ]
    }
   ],
   "source": [
    "# Building model with LGBMRegressor\n",
    "model = LGBMRegressor()\n",
    "\n",
    "# Fitting the model\n",
    "model.fit(Features_train,Target_train)\n",
    "\n",
    "# Predicting the model\n",
    "Target_pred = model.predict(Features_test)\n",
    "\n",
    "# Calculating R-Squared\n",
    "r2_score = metrics.r2_score(Target_test,Target_pred)\n",
    "print(\"R-Squared:\",r2_score)\n",
    "\n",
    "# Calculating Mean Absolute Error\n",
    "mae = metrics.mean_absolute_error(Target_test,Target_pred)\n",
    "print(\"Mean Absolute Error:\",mae)\n",
    "\n",
    "# Calculating Mean Squared Error\n",
    "mse = metrics.mean_squared_error(Target_test,Target_pred)\n",
    "print(\"Mean Squared Error:\",mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ecac4c03",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:49:31.861481Z",
     "iopub.status.busy": "2024-01-11T12:49:31.860931Z",
     "iopub.status.idle": "2024-01-11T12:49:31.874458Z",
     "shell.execute_reply": "2024-01-11T12:49:31.873221Z"
    },
    "papermill": {
     "duration": 0.049979,
     "end_time": "2024-01-11T12:49:31.877463",
     "exception": false,
     "start_time": "2024-01-11T12:49:31.827484",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['XGBRegressor.joblib']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Saving the model\n",
    "from joblib import dump\n",
    "dump(XGBRModel, 'XGBRegressor.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "29ed6c5d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:49:31.943767Z",
     "iopub.status.busy": "2024-01-11T12:49:31.942743Z",
     "iopub.status.idle": "2024-01-11T12:49:31.998913Z",
     "shell.execute_reply": "2024-01-11T12:49:31.997583Z"
    },
    "papermill": {
     "duration": 0.093917,
     "end_time": "2024-01-11T12:49:32.003040",
     "exception": false,
     "start_time": "2024-01-11T12:49:31.909123",
     "status": "completed"
    },
    "tags": []
   },
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDW58</td>\n",
       "      <td>20.75</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.01</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>107.86</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FDW14</td>\n",
       "      <td>8.30</td>\n",
       "      <td>reg</td>\n",
       "      <td>0.04</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>87.32</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>2007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NCN55</td>\n",
       "      <td>14.60</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.10</td>\n",
       "      <td>Others</td>\n",
       "      <td>241.75</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDQ58</td>\n",
       "      <td>7.32</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.02</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>155.03</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>2007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FDY38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.12</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>234.23</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FDH56</td>\n",
       "      <td>9.80</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.06</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>117.15</td>\n",
       "      <td>OUT046</td>\n",
       "      <td>1997</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>FDL48</td>\n",
       "      <td>19.35</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.08</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>50.10</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FDC48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.02</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>81.06</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>FDN33</td>\n",
       "      <td>6.30</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.12</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>95.74</td>\n",
       "      <td>OUT045</td>\n",
       "      <td>2002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FDA36</td>\n",
       "      <td>5.99</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.01</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>186.89</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>2007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0           FDW58        20.75          Low Fat             0.01   \n",
       "1           FDW14         8.30              reg             0.04   \n",
       "2           NCN55        14.60          Low Fat             0.10   \n",
       "3           FDQ58         7.32          Low Fat             0.02   \n",
       "4           FDY38          NaN          Regular             0.12   \n",
       "5           FDH56         9.80          Regular             0.06   \n",
       "6           FDL48        19.35          Regular             0.08   \n",
       "7           FDC48          NaN          Low Fat             0.02   \n",
       "8           FDN33         6.30          Regular             0.12   \n",
       "9           FDA36         5.99          Low Fat             0.01   \n",
       "\n",
       "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "0            Snack Foods    107.86            OUT049   \n",
       "1                  Dairy     87.32            OUT017   \n",
       "2                 Others    241.75            OUT010   \n",
       "3            Snack Foods    155.03            OUT017   \n",
       "4                  Dairy    234.23            OUT027   \n",
       "5  Fruits and Vegetables    117.15            OUT046   \n",
       "6           Baking Goods     50.10            OUT018   \n",
       "7           Baking Goods     81.06            OUT027   \n",
       "8            Snack Foods     95.74            OUT045   \n",
       "9           Baking Goods    186.89            OUT017   \n",
       "\n",
       "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0                       1999      Medium               Tier 1   \n",
       "1                       2007         NaN               Tier 2   \n",
       "2                       1998         NaN               Tier 3   \n",
       "3                       2007         NaN               Tier 2   \n",
       "4                       1985      Medium               Tier 3   \n",
       "5                       1997       Small               Tier 1   \n",
       "6                       2009      Medium               Tier 3   \n",
       "7                       1985      Medium               Tier 3   \n",
       "8                       2002         NaN               Tier 2   \n",
       "9                       2007         NaN               Tier 2   \n",
       "\n",
       "         Outlet_Type  \n",
       "0  Supermarket Type1  \n",
       "1  Supermarket Type1  \n",
       "2      Grocery Store  \n",
       "3  Supermarket Type1  \n",
       "4  Supermarket Type3  \n",
       "5  Supermarket Type1  \n",
       "6  Supermarket Type2  \n",
       "7  Supermarket Type3  \n",
       "8  Supermarket Type1  \n",
       "9  Supermarket Type1  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Making a predictive system\n",
    "# Read test data\n",
    "df_test = pd.read_csv(\"/kaggle/input/bigmart-sales-data/Test.csv\")\n",
    "df_test.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8f15d95e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:49:32.080221Z",
     "iopub.status.busy": "2024-01-11T12:49:32.079685Z",
     "iopub.status.idle": "2024-01-11T12:49:32.088563Z",
     "shell.execute_reply": "2024-01-11T12:49:32.087029Z"
    },
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     "duration": 0.050097,
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     "exception": false,
     "start_time": "2024-01-11T12:49:32.041240",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5681, 11)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "40994373",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:49:32.159148Z",
     "iopub.status.busy": "2024-01-11T12:49:32.158633Z",
     "iopub.status.idle": "2024-01-11T12:49:32.180810Z",
     "shell.execute_reply": "2024-01-11T12:49:32.179168Z"
    },
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     "duration": 0.059356,
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     "exception": false,
     "start_time": "2024-01-11T12:49:32.124433",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5681 entries, 0 to 5680\n",
      "Data columns (total 11 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Item_Identifier            5681 non-null   object \n",
      " 1   Item_Weight                4705 non-null   float64\n",
      " 2   Item_Fat_Content           5681 non-null   object \n",
      " 3   Item_Visibility            5681 non-null   float64\n",
      " 4   Item_Type                  5681 non-null   object \n",
      " 5   Item_MRP                   5681 non-null   float64\n",
      " 6   Outlet_Identifier          5681 non-null   object \n",
      " 7   Outlet_Establishment_Year  5681 non-null   int64  \n",
      " 8   Outlet_Size                4075 non-null   object \n",
      " 9   Outlet_Location_Type       5681 non-null   object \n",
      " 10  Outlet_Type                5681 non-null   object \n",
      "dtypes: float64(3), int64(1), object(7)\n",
      "memory usage: 488.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing test data\n",
    "df_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "82c7bb71",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:49:32.254962Z",
     "iopub.status.busy": "2024-01-11T12:49:32.254501Z",
     "iopub.status.idle": "2024-01-11T12:49:32.269324Z",
     "shell.execute_reply": "2024-01-11T12:49:32.267047Z"
    },
    "papermill": {
     "duration": 0.053844,
     "end_time": "2024-01-11T12:49:32.272340",
     "exception": false,
     "start_time": "2024-01-11T12:49:32.218496",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Fat_Content\n",
       "Low Fat    3396\n",
       "Regular    1935\n",
       "LF          206\n",
       "reg          78\n",
       "low fat      66\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See categories in Item_Fat_Content column\n",
    "df_test['Item_Fat_Content'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "fe78e382",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:49:32.342531Z",
     "iopub.status.busy": "2024-01-11T12:49:32.342042Z",
     "iopub.status.idle": "2024-01-11T12:49:32.352308Z",
     "shell.execute_reply": "2024-01-11T12:49:32.350898Z"
    },
    "papermill": {
     "duration": 0.048654,
     "end_time": "2024-01-11T12:49:32.354751",
     "exception": false,
     "start_time": "2024-01-11T12:49:32.306097",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Handle Item_Fat_Content column categories with replacement\n",
    "df_test['Item_Fat_Content'].replace({'LF':'Low Fat','reg':'Regular','low fat':'Low Fat'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f27da23a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:49:32.423763Z",
     "iopub.status.busy": "2024-01-11T12:49:32.423302Z",
     "iopub.status.idle": "2024-01-11T12:49:32.435704Z",
     "shell.execute_reply": "2024-01-11T12:49:32.434231Z"
    },
    "papermill": {
     "duration": 0.050704,
     "end_time": "2024-01-11T12:49:32.438491",
     "exception": false,
     "start_time": "2024-01-11T12:49:32.387787",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Fat_Content\n",
       "Low Fat    3668\n",
       "Regular    2013\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See categories in Item_Fat_Content column\n",
    "df_test['Item_Fat_Content'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "36b0a8df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:49:32.509152Z",
     "iopub.status.busy": "2024-01-11T12:49:32.508553Z",
     "iopub.status.idle": "2024-01-11T12:49:32.540767Z",
     "shell.execute_reply": "2024-01-11T12:49:32.539334Z"
    },
    "papermill": {
     "duration": 0.071174,
     "end_time": "2024-01-11T12:49:32.544031",
     "exception": false,
     "start_time": "2024-01-11T12:49:32.472857",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Appling Label Encoding on categorical columns\n",
    "le = LabelEncoder()\n",
    "\n",
    "df_test['Item_Fat_Content'] = le.fit_transform(df_test['Item_Fat_Content'])\n",
    "\n",
    "df_test['Item_Type'] = le.fit_transform(df_test['Item_Type'])\n",
    "\n",
    "df_test['Outlet_Size'] = le.fit_transform(df_test['Outlet_Size'])\n",
    "\n",
    "df_test['Outlet_Location_Type'] = le.fit_transform(df_test['Outlet_Location_Type'])\n",
    "\n",
    "df_test['Outlet_Type'] = le.fit_transform(df_test['Outlet_Type'])\n",
    "\n",
    "df_test['Item_Identifier'] = le.fit_transform(df_test['Item_Identifier'])\n",
    "\n",
    "df_test['Outlet_Identifier'] = le.fit_transform(df_test['Outlet_Identifier'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c664faed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:49:32.613556Z",
     "iopub.status.busy": "2024-01-11T12:49:32.613104Z",
     "iopub.status.idle": "2024-01-11T12:49:32.631953Z",
     "shell.execute_reply": "2024-01-11T12:49:32.630487Z"
    },
    "papermill": {
     "duration": 0.057454,
     "end_time": "2024-01-11T12:49:32.634875",
     "exception": false,
     "start_time": "2024-01-11T12:49:32.577421",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5681 entries, 0 to 5680\n",
      "Data columns (total 11 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Item_Identifier            5681 non-null   int64  \n",
      " 1   Item_Weight                4705 non-null   float64\n",
      " 2   Item_Fat_Content           5681 non-null   int64  \n",
      " 3   Item_Visibility            5681 non-null   float64\n",
      " 4   Item_Type                  5681 non-null   int64  \n",
      " 5   Item_MRP                   5681 non-null   float64\n",
      " 6   Outlet_Identifier          5681 non-null   int64  \n",
      " 7   Outlet_Establishment_Year  5681 non-null   int64  \n",
      " 8   Outlet_Size                5681 non-null   int64  \n",
      " 9   Outlet_Location_Type       5681 non-null   int64  \n",
      " 10  Outlet_Type                5681 non-null   int64  \n",
      "dtypes: float64(3), int64(8)\n",
      "memory usage: 488.3 KB\n"
     ]
    }
   ],
   "source": [
    "df_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8a146292",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:49:32.704417Z",
     "iopub.status.busy": "2024-01-11T12:49:32.703673Z",
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     "shell.execute_reply": "2024-01-11T12:49:32.724233Z"
    },
    "papermill": {
     "duration": 0.05909,
     "end_time": "2024-01-11T12:49:32.727351",
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     "start_time": "2024-01-11T12:49:32.668261",
     "status": "completed"
    },
    "tags": []
   },
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1103</td>\n",
       "      <td>1.73</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.13</td>\n",
       "      <td>13</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>9</td>\n",
       "      <td>1999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1067</td>\n",
       "      <td>-0.94</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>2</td>\n",
       "      <td>2007</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1406</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0</td>\n",
       "      <td>0.66</td>\n",
       "      <td>11</td>\n",
       "      <td>1.63</td>\n",
       "      <td>0</td>\n",
       "      <td>1998</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>809</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>13</td>\n",
       "      <td>0.23</td>\n",
       "      <td>2</td>\n",
       "      <td>2007</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1184</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1.03</td>\n",
       "      <td>4</td>\n",
       "      <td>1.51</td>\n",
       "      <td>5</td>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>461</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>8</td>\n",
       "      <td>1997</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>604</td>\n",
       "      <td>1.43</td>\n",
       "      <td>1</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.47</td>\n",
       "      <td>3</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>266</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>5</td>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>668</td>\n",
       "      <td>-1.37</td>\n",
       "      <td>1</td>\n",
       "      <td>1.13</td>\n",
       "      <td>13</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>7</td>\n",
       "      <td>2002</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>170</td>\n",
       "      <td>-1.44</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.17</td>\n",
       "      <td>0</td>\n",
       "      <td>0.74</td>\n",
       "      <td>2</td>\n",
       "      <td>2007</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Identifier  Item_Weight  Item_Fat_Content  Item_Visibility  Item_Type  \\\n",
       "0             1103         1.73                 0            -1.13         13   \n",
       "1             1067        -0.94                 1            -0.53          4   \n",
       "2             1406         0.41                 0             0.66         11   \n",
       "3              809        -1.15                 0            -0.98         13   \n",
       "4             1184          NaN                 1             1.03          4   \n",
       "5              461        -0.62                 1            -0.04          6   \n",
       "6              604         1.43                 1             0.33          0   \n",
       "7              266          NaN                 0            -0.97          0   \n",
       "8              668        -1.37                 1             1.13         13   \n",
       "9              170        -1.44                 0            -1.17          0   \n",
       "\n",
       "   Item_MRP  Outlet_Identifier  Outlet_Establishment_Year  Outlet_Size  \\\n",
       "0     -0.54                  9                       1999            1   \n",
       "1     -0.87                  2                       2007            3   \n",
       "2      1.63                  0                       1998            3   \n",
       "3      0.23                  2                       2007            3   \n",
       "4      1.51                  5                       1985            1   \n",
       "5     -0.39                  8                       1997            2   \n",
       "6     -1.47                  3                       2009            1   \n",
       "7     -0.97                  5                       1985            1   \n",
       "8     -0.73                  7                       2002            3   \n",
       "9      0.74                  2                       2007            3   \n",
       "\n",
       "   Outlet_Location_Type  Outlet_Type  \n",
       "0                     0            1  \n",
       "1                     1            1  \n",
       "2                     2            0  \n",
       "3                     1            1  \n",
       "4                     2            3  \n",
       "5                     0            1  \n",
       "6                     2            2  \n",
       "7                     2            3  \n",
       "8                     1            1  \n",
       "9                     1            1  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc = StandardScaler()\n",
    "\n",
    "df_test['Item_Weight'] = sc.fit_transform(df_test['Item_Weight'].values.reshape(-1,1))\n",
    "\n",
    "df_test['Item_Visibility'] = sc.fit_transform(df_test['Item_Visibility'].values.reshape(-1,1))\n",
    "\n",
    "df_test['Item_MRP'] = sc.fit_transform(df_test['Item_MRP'].values.reshape(-1,1))\n",
    "\n",
    "df_test.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d6aca891",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:49:32.796493Z",
     "iopub.status.busy": "2024-01-11T12:49:32.795735Z",
     "iopub.status.idle": "2024-01-11T12:49:32.827760Z",
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    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Loading trained model\n",
    "from joblib import load\n",
    "\n",
    "model = load('XGBRegressor.joblib')\n",
    "\n",
    "#predict the model\n",
    "Target_pred = model.predict(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "8d464494",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T12:49:32.899686Z",
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    },
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     "duration": 0.050864,
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "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_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1103</td>\n",
       "      <td>-0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1067</td>\n",
       "      <td>-0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1406</td>\n",
       "      <td>-1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>809</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1184</td>\n",
       "      <td>3.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5676</th>\n",
       "      <td>231</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5677</th>\n",
       "      <td>306</td>\n",
       "      <td>-0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5678</th>\n",
       "      <td>1412</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5679</th>\n",
       "      <td>517</td>\n",
       "      <td>1.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5680</th>\n",
       "      <td>987</td>\n",
       "      <td>-0.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5681 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Item_Identifier  Item_Outlet_Sales\n",
       "0                1103              -0.32\n",
       "1                1067              -0.57\n",
       "2                1406              -1.09\n",
       "3                 809               0.08\n",
       "4                1184               3.16\n",
       "...               ...                ...\n",
       "5676              231               0.10\n",
       "5677              306              -0.16\n",
       "5678             1412               0.02\n",
       "5679              517               1.22\n",
       "5680              987              -0.34\n",
       "\n",
       "[5681 rows x 2 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print each Item_Identifier with its predicted value and put the result in dataframe\n",
    "df = pd.DataFrame({'Item_Identifier':df_test['Item_Identifier'],'Item_Outlet_Sales':Target_pred})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d41b45ae",
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