team-riverborn-solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5ccb2cc7",
   "metadata": {
    "papermill": {
     "duration": 0.014603,
     "end_time": "2024-05-17T05:47:13.693900",
     "exception": false,
     "start_time": "2024-05-17T05:47:13.679297",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Step 1: Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e42ac35d",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2024-05-17T05:47:13.723874Z",
     "iopub.status.busy": "2024-05-17T05:47:13.723481Z",
     "iopub.status.idle": "2024-05-17T05:47:16.299418Z",
     "shell.execute_reply": "2024-05-17T05:47:16.298409Z"
    },
    "papermill": {
     "duration": 2.594188,
     "end_time": "2024-05-17T05:47:16.302160",
     "exception": false,
     "start_time": "2024-05-17T05:47:13.707972",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.multiclass import OneVsOneClassifier,OneVsRestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score,classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d96d9e33",
   "metadata": {
    "papermill": {
     "duration": 0.013559,
     "end_time": "2024-05-17T05:47:16.330867",
     "exception": false,
     "start_time": "2024-05-17T05:47:16.317308",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Step 2: Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9190a310",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:47:16.360435Z",
     "iopub.status.busy": "2024-05-17T05:47:16.359928Z",
     "iopub.status.idle": "2024-05-17T05:47:17.878250Z",
     "shell.execute_reply": "2024-05-17T05:47:17.876656Z"
    },
    "papermill": {
     "duration": 1.536437,
     "end_time": "2024-05-17T05:47:17.881144",
     "exception": false,
     "start_time": "2024-05-17T05:47:16.344707",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_18/433442297.py:1: DtypeWarning: Columns (3,4,5) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  train_data = pd.read_csv('/kaggle/input/data-storm-5-0/train_kaggle.csv')\n"
     ]
    },
    {
     "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>Customer_ID</th>\n",
       "      <th>outlet_city</th>\n",
       "      <th>luxury_sales</th>\n",
       "      <th>fresh_sales</th>\n",
       "      <th>dry_sales</th>\n",
       "      <th>cluster_catgeory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10493832.0</td>\n",
       "      <td>Kelaniya</td>\n",
       "      <td>1209.6</td>\n",
       "      <td>756.0</td>\n",
       "      <td>5292.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10178643.0</td>\n",
       "      <td>Moratuwa</td>\n",
       "      <td>1590.12</td>\n",
       "      <td>1060.08</td>\n",
       "      <td>6007.12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10513916.0</td>\n",
       "      <td>Wattala</td>\n",
       "      <td>2288.88</td>\n",
       "      <td>1481.04</td>\n",
       "      <td>9155.52</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10334589.0</td>\n",
       "      <td>Wattala</td>\n",
       "      <td>2274.94</td>\n",
       "      <td>1739.66</td>\n",
       "      <td>9099.76</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10458365.0</td>\n",
       "      <td>Kelaniya</td>\n",
       "      <td>2345.49</td>\n",
       "      <td>2069.55</td>\n",
       "      <td>9243.99</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer_ID outlet_city luxury_sales fresh_sales dry_sales cluster_catgeory\n",
       "0   10493832.0    Kelaniya       1209.6       756.0    5292.0                4\n",
       "1   10178643.0    Moratuwa      1590.12     1060.08   6007.12                1\n",
       "2   10513916.0     Wattala      2288.88     1481.04   9155.52                4\n",
       "3   10334589.0     Wattala      2274.94     1739.66   9099.76                4\n",
       "4   10458365.0    Kelaniya      2345.49     2069.55   9243.99                4"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = pd.read_csv('/kaggle/input/data-storm-5-0/train_kaggle.csv')\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25e94a19",
   "metadata": {
    "papermill": {
     "duration": 0.013985,
     "end_time": "2024-05-17T05:47:17.909368",
     "exception": false,
     "start_time": "2024-05-17T05:47:17.895383",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Set Customer ID as Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2ab32aa3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:47:17.939950Z",
     "iopub.status.busy": "2024-05-17T05:47:17.939547Z",
     "iopub.status.idle": "2024-05-17T05:47:17.947573Z",
     "shell.execute_reply": "2024-05-17T05:47:17.946424Z"
    },
    "papermill": {
     "duration": 0.026398,
     "end_time": "2024-05-17T05:47:17.949817",
     "exception": false,
     "start_time": "2024-05-17T05:47:17.923419",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_data.set_index('Customer_ID',inplace=True)\n",
    "train_data.index.name = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8182b635",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:47:17.980505Z",
     "iopub.status.busy": "2024-05-17T05:47:17.980148Z",
     "iopub.status.idle": "2024-05-17T05:47:17.992571Z",
     "shell.execute_reply": "2024-05-17T05:47:17.991496Z"
    },
    "papermill": {
     "duration": 0.030618,
     "end_time": "2024-05-17T05:47:17.994689",
     "exception": false,
     "start_time": "2024-05-17T05:47:17.964071",
     "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>outlet_city</th>\n",
       "      <th>luxury_sales</th>\n",
       "      <th>fresh_sales</th>\n",
       "      <th>dry_sales</th>\n",
       "      <th>cluster_catgeory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10493832.0</th>\n",
       "      <td>Kelaniya</td>\n",
       "      <td>1209.6</td>\n",
       "      <td>756.0</td>\n",
       "      <td>5292.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10178643.0</th>\n",
       "      <td>Moratuwa</td>\n",
       "      <td>1590.12</td>\n",
       "      <td>1060.08</td>\n",
       "      <td>6007.12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10513916.0</th>\n",
       "      <td>Wattala</td>\n",
       "      <td>2288.88</td>\n",
       "      <td>1481.04</td>\n",
       "      <td>9155.52</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10334589.0</th>\n",
       "      <td>Wattala</td>\n",
       "      <td>2274.94</td>\n",
       "      <td>1739.66</td>\n",
       "      <td>9099.76</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10458365.0</th>\n",
       "      <td>Kelaniya</td>\n",
       "      <td>2345.49</td>\n",
       "      <td>2069.55</td>\n",
       "      <td>9243.99</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           outlet_city luxury_sales fresh_sales dry_sales cluster_catgeory\n",
       "10493832.0    Kelaniya       1209.6       756.0    5292.0                4\n",
       "10178643.0    Moratuwa      1590.12     1060.08   6007.12                1\n",
       "10513916.0     Wattala      2288.88     1481.04   9155.52                4\n",
       "10334589.0     Wattala      2274.94     1739.66   9099.76                4\n",
       "10458365.0    Kelaniya      2345.49     2069.55   9243.99                4"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aeab7578",
   "metadata": {
    "papermill": {
     "duration": 0.013906,
     "end_time": "2024-05-17T05:47:18.022907",
     "exception": false,
     "start_time": "2024-05-17T05:47:18.009001",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Step 3: Preprocessing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6f4497eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:47:18.052815Z",
     "iopub.status.busy": "2024-05-17T05:47:18.052415Z",
     "iopub.status.idle": "2024-05-17T05:47:18.060251Z",
     "shell.execute_reply": "2024-05-17T05:47:18.059218Z"
    },
    "papermill": {
     "duration": 0.025586,
     "end_time": "2024-05-17T05:47:18.062645",
     "exception": false,
     "start_time": "2024-05-17T05:47:18.037059",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "outlet_city         object\n",
       "luxury_sales        object\n",
       "fresh_sales         object\n",
       "dry_sales           object\n",
       "cluster_catgeory    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3f287a4",
   "metadata": {
    "papermill": {
     "duration": 0.014778,
     "end_time": "2024-05-17T05:47:18.092005",
     "exception": false,
     "start_time": "2024-05-17T05:47:18.077227",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Change the numeric data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "59958bfb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:47:18.123516Z",
     "iopub.status.busy": "2024-05-17T05:47:18.123123Z",
     "iopub.status.idle": "2024-05-17T05:47:19.082731Z",
     "shell.execute_reply": "2024-05-17T05:47:19.081655Z"
    },
    "papermill": {
     "duration": 0.978241,
     "end_time": "2024-05-17T05:47:19.085168",
     "exception": false,
     "start_time": "2024-05-17T05:47:18.106927",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_data['luxury_sales'] = pd.to_numeric(train_data['luxury_sales'], errors='coerce')\n",
    "train_data['fresh_sales'] = pd.to_numeric(train_data['fresh_sales'], errors='coerce')\n",
    "train_data['dry_sales'] = pd.to_numeric(train_data['dry_sales'], errors='coerce')\n",
    "train_data['cluster_catgeory'] = pd.to_numeric(train_data['cluster_catgeory'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5dddfb7b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:47:19.116925Z",
     "iopub.status.busy": "2024-05-17T05:47:19.116513Z",
     "iopub.status.idle": "2024-05-17T05:47:19.123717Z",
     "shell.execute_reply": "2024-05-17T05:47:19.122738Z"
    },
    "papermill": {
     "duration": 0.025918,
     "end_time": "2024-05-17T05:47:19.126079",
     "exception": false,
     "start_time": "2024-05-17T05:47:19.100161",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "outlet_city          object\n",
       "luxury_sales        float64\n",
       "fresh_sales         float64\n",
       "dry_sales           float64\n",
       "cluster_catgeory    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8de0d0a7",
   "metadata": {
    "papermill": {
     "duration": 0.014279,
     "end_time": "2024-05-17T05:47:19.154977",
     "exception": false,
     "start_time": "2024-05-17T05:47:19.140698",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Dealing with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "da2a62a4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:47:19.186227Z",
     "iopub.status.busy": "2024-05-17T05:47:19.185230Z",
     "iopub.status.idle": "2024-05-17T05:47:19.235251Z",
     "shell.execute_reply": "2024-05-17T05:47:19.234120Z"
    },
    "papermill": {
     "duration": 0.068042,
     "end_time": "2024-05-17T05:47:19.237517",
     "exception": false,
     "start_time": "2024-05-17T05:47:19.169475",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "outlet_city          2\n",
       "luxury_sales        45\n",
       "fresh_sales         50\n",
       "dry_sales           38\n",
       "cluster_catgeory     2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "48907c53",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:47:19.272204Z",
     "iopub.status.busy": "2024-05-17T05:47:19.271215Z",
     "iopub.status.idle": "2024-05-17T05:47:19.277633Z",
     "shell.execute_reply": "2024-05-17T05:47:19.276762Z"
    },
    "papermill": {
     "duration": 0.026412,
     "end_time": "2024-05-17T05:47:19.279467",
     "exception": false,
     "start_time": "2024-05-17T05:47:19.253055",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(774155, 5)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af7c9ad1",
   "metadata": {
    "papermill": {
     "duration": 0.014516,
     "end_time": "2024-05-17T05:47:19.308580",
     "exception": false,
     "start_time": "2024-05-17T05:47:19.294064",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Since shape is too large comparing to missing values, we can drop those"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "06d5ae1e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:47:19.340247Z",
     "iopub.status.busy": "2024-05-17T05:47:19.339368Z",
     "iopub.status.idle": "2024-05-17T05:47:19.433169Z",
     "shell.execute_reply": "2024-05-17T05:47:19.431919Z"
    },
    "papermill": {
     "duration": 0.112361,
     "end_time": "2024-05-17T05:47:19.435733",
     "exception": false,
     "start_time": "2024-05-17T05:47:19.323372",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f2c3a650",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:47:19.467565Z",
     "iopub.status.busy": "2024-05-17T05:47:19.466462Z",
     "iopub.status.idle": "2024-05-17T05:47:19.473203Z",
     "shell.execute_reply": "2024-05-17T05:47:19.472306Z"
    },
    "papermill": {
     "duration": 0.024666,
     "end_time": "2024-05-17T05:47:19.475136",
     "exception": false,
     "start_time": "2024-05-17T05:47:19.450470",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(774040, 5)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b432b25",
   "metadata": {
    "papermill": {
     "duration": 0.014672,
     "end_time": "2024-05-17T05:47:19.504612",
     "exception": false,
     "start_time": "2024-05-17T05:47:19.489940",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Drop duplicates as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d48cc8a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:47:19.537298Z",
     "iopub.status.busy": "2024-05-17T05:47:19.536936Z",
     "iopub.status.idle": "2024-05-17T05:47:19.857140Z",
     "shell.execute_reply": "2024-05-17T05:47:19.855968Z"
    },
    "papermill": {
     "duration": 0.340403,
     "end_time": "2024-05-17T05:47:19.859851",
     "exception": false,
     "start_time": "2024-05-17T05:47:19.519448",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_data.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b4505c9e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:47:19.949032Z",
     "iopub.status.busy": "2024-05-17T05:47:19.948632Z",
     "iopub.status.idle": "2024-05-17T05:47:19.954865Z",
     "shell.execute_reply": "2024-05-17T05:47:19.954103Z"
    },
    "papermill": {
     "duration": 0.081471,
     "end_time": "2024-05-17T05:47:19.956894",
     "exception": false,
     "start_time": "2024-05-17T05:47:19.875423",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(771100, 5)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "639ad1ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:47:20.000470Z",
     "iopub.status.busy": "2024-05-17T05:47:19.999498Z",
     "iopub.status.idle": "2024-05-17T05:47:20.072629Z",
     "shell.execute_reply": "2024-05-17T05:47:20.071464Z"
    },
    "papermill": {
     "duration": 0.099185,
     "end_time": "2024-05-17T05:47:20.074913",
     "exception": false,
     "start_time": "2024-05-17T05:47:19.975728",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "outlet_city\n",
       "Batticaloa                40866\n",
       "Colombo                   40889\n",
       "Dehiwala-Mount Lavinia    36676\n",
       "Galle                     40885\n",
       "Gampaha                   40116\n",
       "Homagama                  36479\n",
       "Jaffna                    40548\n",
       "Kaduwela                  36502\n",
       "Kalmunai                  40590\n",
       "Kandy                     40393\n",
       "Katunayake                40519\n",
       "Kelaniya                  35818\n",
       "Kotte                     36633\n",
       "Moratuwa                  36248\n",
       "Negombo                   40558\n",
       "Nuwara Eliya              36926\n",
       "Panadura                  36552\n",
       "Peliyagoda                36986\n",
       "Trincomalee               40398\n",
       "Wattala                   36518\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['outlet_city'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1072d455",
   "metadata": {
    "papermill": {
     "duration": 0.014994,
     "end_time": "2024-05-17T05:47:20.105452",
     "exception": false,
     "start_time": "2024-05-17T05:47:20.090458",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Remove outlier classes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "69d18ed9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:47:20.138120Z",
     "iopub.status.busy": "2024-05-17T05:47:20.137308Z",
     "iopub.status.idle": "2024-05-17T05:47:20.160095Z",
     "shell.execute_reply": "2024-05-17T05:47:20.159231Z"
    },
    "papermill": {
     "duration": 0.041517,
     "end_time": "2024-05-17T05:47:20.162179",
     "exception": false,
     "start_time": "2024-05-17T05:47:20.120662",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster_catgeory\n",
       "1.0      188297\n",
       "4.0      171485\n",
       "6.0      168624\n",
       "2.0      154355\n",
       "3.0       48842\n",
       "5.0       39492\n",
       "99.0          1\n",
       "95.0          1\n",
       "98.0          1\n",
       "100.0         1\n",
       "89.0          1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['cluster_catgeory'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9b2eea57",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:47:20.194998Z",
     "iopub.status.busy": "2024-05-17T05:47:20.194034Z",
     "iopub.status.idle": "2024-05-17T05:47:21.264866Z",
     "shell.execute_reply": "2024-05-17T05:47:21.263698Z"
    },
    "papermill": {
     "duration": 1.090045,
     "end_time": "2024-05-17T05:47:21.267546",
     "exception": false,
     "start_time": "2024-05-17T05:47:20.177501",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_data.drop(train_data[train_data['cluster_catgeory']==99.0].index,inplace=True)\n",
    "train_data.drop(train_data[train_data['cluster_catgeory']==95.0].index,inplace=True)\n",
    "train_data.drop(train_data[train_data['cluster_catgeory']==98.0].index,inplace=True)\n",
    "train_data.drop(train_data[train_data['cluster_catgeory']==100.0].index,inplace=True)\n",
    "train_data.drop(train_data[train_data['cluster_catgeory']==89.0].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d13f5ef2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:47:21.300975Z",
     "iopub.status.busy": "2024-05-17T05:47:21.300575Z",
     "iopub.status.idle": "2024-05-17T05:47:21.323005Z",
     "shell.execute_reply": "2024-05-17T05:47:21.321831Z"
    },
    "papermill": {
     "duration": 0.042518,
     "end_time": "2024-05-17T05:47:21.325438",
     "exception": false,
     "start_time": "2024-05-17T05:47:21.282920",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster_catgeory\n",
       "1.0    188297\n",
       "2.0    154355\n",
       "3.0     48842\n",
       "4.0    171485\n",
       "5.0     39492\n",
       "6.0    168624\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['cluster_catgeory'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a80cb31",
   "metadata": {
    "papermill": {
     "duration": 0.015104,
     "end_time": "2024-05-17T05:47:21.356187",
     "exception": false,
     "start_time": "2024-05-17T05:47:21.341083",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Step 4: Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3ee4655",
   "metadata": {
    "papermill": {
     "duration": 0.015074,
     "end_time": "2024-05-17T05:47:21.386742",
     "exception": false,
     "start_time": "2024-05-17T05:47:21.371668",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Handle categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4a9605e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:47:21.419178Z",
     "iopub.status.busy": "2024-05-17T05:47:21.418773Z",
     "iopub.status.idle": "2024-05-17T05:47:21.557348Z",
     "shell.execute_reply": "2024-05-17T05:47:21.556162Z"
    },
    "papermill": {
     "duration": 0.15754,
     "end_time": "2024-05-17T05:47:21.559704",
     "exception": false,
     "start_time": "2024-05-17T05:47:21.402164",
     "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>luxury_sales</th>\n",
       "      <th>fresh_sales</th>\n",
       "      <th>dry_sales</th>\n",
       "      <th>cluster_catgeory</th>\n",
       "      <th>outlet_city_Batticaloa</th>\n",
       "      <th>outlet_city_Colombo</th>\n",
       "      <th>outlet_city_Dehiwala-Mount Lavinia</th>\n",
       "      <th>outlet_city_Galle</th>\n",
       "      <th>outlet_city_Gampaha</th>\n",
       "      <th>outlet_city_Homagama</th>\n",
       "      <th>...</th>\n",
       "      <th>outlet_city_Katunayake</th>\n",
       "      <th>outlet_city_Kelaniya</th>\n",
       "      <th>outlet_city_Kotte</th>\n",
       "      <th>outlet_city_Moratuwa</th>\n",
       "      <th>outlet_city_Negombo</th>\n",
       "      <th>outlet_city_Nuwara Eliya</th>\n",
       "      <th>outlet_city_Panadura</th>\n",
       "      <th>outlet_city_Peliyagoda</th>\n",
       "      <th>outlet_city_Trincomalee</th>\n",
       "      <th>outlet_city_Wattala</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10493832.0</th>\n",
       "      <td>1209.60</td>\n",
       "      <td>756.00</td>\n",
       "      <td>5292.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10178643.0</th>\n",
       "      <td>1590.12</td>\n",
       "      <td>1060.08</td>\n",
       "      <td>6007.12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10513916.0</th>\n",
       "      <td>2288.88</td>\n",
       "      <td>1481.04</td>\n",
       "      <td>9155.52</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10334589.0</th>\n",
       "      <td>2274.94</td>\n",
       "      <td>1739.66</td>\n",
       "      <td>9099.76</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10458365.0</th>\n",
       "      <td>2345.49</td>\n",
       "      <td>2069.55</td>\n",
       "      <td>9243.99</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            luxury_sales  fresh_sales  dry_sales  cluster_catgeory  \\\n",
       "10493832.0       1209.60       756.00    5292.00               4.0   \n",
       "10178643.0       1590.12      1060.08    6007.12               1.0   \n",
       "10513916.0       2288.88      1481.04    9155.52               4.0   \n",
       "10334589.0       2274.94      1739.66    9099.76               4.0   \n",
       "10458365.0       2345.49      2069.55    9243.99               4.0   \n",
       "\n",
       "            outlet_city_Batticaloa  outlet_city_Colombo  \\\n",
       "10493832.0                   False                False   \n",
       "10178643.0                   False                False   \n",
       "10513916.0                   False                False   \n",
       "10334589.0                   False                False   \n",
       "10458365.0                   False                False   \n",
       "\n",
       "            outlet_city_Dehiwala-Mount Lavinia  outlet_city_Galle  \\\n",
       "10493832.0                               False              False   \n",
       "10178643.0                               False              False   \n",
       "10513916.0                               False              False   \n",
       "10334589.0                               False              False   \n",
       "10458365.0                               False              False   \n",
       "\n",
       "            outlet_city_Gampaha  outlet_city_Homagama  ...  \\\n",
       "10493832.0                False                 False  ...   \n",
       "10178643.0                False                 False  ...   \n",
       "10513916.0                False                 False  ...   \n",
       "10334589.0                False                 False  ...   \n",
       "10458365.0                False                 False  ...   \n",
       "\n",
       "            outlet_city_Katunayake  outlet_city_Kelaniya  outlet_city_Kotte  \\\n",
       "10493832.0                   False                  True              False   \n",
       "10178643.0                   False                 False              False   \n",
       "10513916.0                   False                 False              False   \n",
       "10334589.0                   False                 False              False   \n",
       "10458365.0                   False                  True              False   \n",
       "\n",
       "            outlet_city_Moratuwa  outlet_city_Negombo  \\\n",
       "10493832.0                 False                False   \n",
       "10178643.0                  True                False   \n",
       "10513916.0                 False                False   \n",
       "10334589.0                 False                False   \n",
       "10458365.0                 False                False   \n",
       "\n",
       "            outlet_city_Nuwara Eliya  outlet_city_Panadura  \\\n",
       "10493832.0                     False                 False   \n",
       "10178643.0                     False                 False   \n",
       "10513916.0                     False                 False   \n",
       "10334589.0                     False                 False   \n",
       "10458365.0                     False                 False   \n",
       "\n",
       "            outlet_city_Peliyagoda  outlet_city_Trincomalee  \\\n",
       "10493832.0                   False                    False   \n",
       "10178643.0                   False                    False   \n",
       "10513916.0                   False                    False   \n",
       "10334589.0                   False                    False   \n",
       "10458365.0                   False                    False   \n",
       "\n",
       "            outlet_city_Wattala  \n",
       "10493832.0                False  \n",
       "10178643.0                False  \n",
       "10513916.0                 True  \n",
       "10334589.0                 True  \n",
       "10458365.0                False  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_f = pd.get_dummies(train_data)\n",
    "train_data_f.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0d4b4df5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:47:21.595066Z",
     "iopub.status.busy": "2024-05-17T05:47:21.594671Z",
     "iopub.status.idle": "2024-05-17T05:47:21.673824Z",
     "shell.execute_reply": "2024-05-17T05:47:21.672804Z"
    },
    "papermill": {
     "duration": 0.10008,
     "end_time": "2024-05-17T05:47:21.676084",
     "exception": false,
     "start_time": "2024-05-17T05:47:21.576004",
     "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>luxury_sales</th>\n",
       "      <th>fresh_sales</th>\n",
       "      <th>dry_sales</th>\n",
       "      <th>cluster_catgeory</th>\n",
       "      <th>outlet_city_Batticaloa</th>\n",
       "      <th>outlet_city_Colombo</th>\n",
       "      <th>outlet_city_Dehiwala-Mount Lavinia</th>\n",
       "      <th>outlet_city_Galle</th>\n",
       "      <th>outlet_city_Gampaha</th>\n",
       "      <th>outlet_city_Homagama</th>\n",
       "      <th>...</th>\n",
       "      <th>outlet_city_Katunayake</th>\n",
       "      <th>outlet_city_Kelaniya</th>\n",
       "      <th>outlet_city_Kotte</th>\n",
       "      <th>outlet_city_Moratuwa</th>\n",
       "      <th>outlet_city_Negombo</th>\n",
       "      <th>outlet_city_Nuwara Eliya</th>\n",
       "      <th>outlet_city_Panadura</th>\n",
       "      <th>outlet_city_Peliyagoda</th>\n",
       "      <th>outlet_city_Trincomalee</th>\n",
       "      <th>outlet_city_Wattala</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10493832.0</th>\n",
       "      <td>1209.60</td>\n",
       "      <td>756.00</td>\n",
       "      <td>5292.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10178643.0</th>\n",
       "      <td>1590.12</td>\n",
       "      <td>1060.08</td>\n",
       "      <td>6007.12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10513916.0</th>\n",
       "      <td>2288.88</td>\n",
       "      <td>1481.04</td>\n",
       "      <td>9155.52</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10334589.0</th>\n",
       "      <td>2274.94</td>\n",
       "      <td>1739.66</td>\n",
       "      <td>9099.76</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10458365.0</th>\n",
       "      <td>2345.49</td>\n",
       "      <td>2069.55</td>\n",
       "      <td>9243.99</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            luxury_sales  fresh_sales  dry_sales  cluster_catgeory  \\\n",
       "10493832.0       1209.60       756.00    5292.00               4.0   \n",
       "10178643.0       1590.12      1060.08    6007.12               1.0   \n",
       "10513916.0       2288.88      1481.04    9155.52               4.0   \n",
       "10334589.0       2274.94      1739.66    9099.76               4.0   \n",
       "10458365.0       2345.49      2069.55    9243.99               4.0   \n",
       "\n",
       "            outlet_city_Batticaloa  outlet_city_Colombo  \\\n",
       "10493832.0                     0.0                  0.0   \n",
       "10178643.0                     0.0                  0.0   \n",
       "10513916.0                     0.0                  0.0   \n",
       "10334589.0                     0.0                  0.0   \n",
       "10458365.0                     0.0                  0.0   \n",
       "\n",
       "            outlet_city_Dehiwala-Mount Lavinia  outlet_city_Galle  \\\n",
       "10493832.0                                 0.0                0.0   \n",
       "10178643.0                                 0.0                0.0   \n",
       "10513916.0                                 0.0                0.0   \n",
       "10334589.0                                 0.0                0.0   \n",
       "10458365.0                                 0.0                0.0   \n",
       "\n",
       "            outlet_city_Gampaha  outlet_city_Homagama  ...  \\\n",
       "10493832.0                  0.0                   0.0  ...   \n",
       "10178643.0                  0.0                   0.0  ...   \n",
       "10513916.0                  0.0                   0.0  ...   \n",
       "10334589.0                  0.0                   0.0  ...   \n",
       "10458365.0                  0.0                   0.0  ...   \n",
       "\n",
       "            outlet_city_Katunayake  outlet_city_Kelaniya  outlet_city_Kotte  \\\n",
       "10493832.0                     0.0                   1.0                0.0   \n",
       "10178643.0                     0.0                   0.0                0.0   \n",
       "10513916.0                     0.0                   0.0                0.0   \n",
       "10334589.0                     0.0                   0.0                0.0   \n",
       "10458365.0                     0.0                   1.0                0.0   \n",
       "\n",
       "            outlet_city_Moratuwa  outlet_city_Negombo  \\\n",
       "10493832.0                   0.0                  0.0   \n",
       "10178643.0                   1.0                  0.0   \n",
       "10513916.0                   0.0                  0.0   \n",
       "10334589.0                   0.0                  0.0   \n",
       "10458365.0                   0.0                  0.0   \n",
       "\n",
       "            outlet_city_Nuwara Eliya  outlet_city_Panadura  \\\n",
       "10493832.0                       0.0                   0.0   \n",
       "10178643.0                       0.0                   0.0   \n",
       "10513916.0                       0.0                   0.0   \n",
       "10334589.0                       0.0                   0.0   \n",
       "10458365.0                       0.0                   0.0   \n",
       "\n",
       "            outlet_city_Peliyagoda  outlet_city_Trincomalee  \\\n",
       "10493832.0                     0.0                      0.0   \n",
       "10178643.0                     0.0                      0.0   \n",
       "10513916.0                     0.0                      0.0   \n",
       "10334589.0                     0.0                      0.0   \n",
       "10458365.0                     0.0                      0.0   \n",
       "\n",
       "            outlet_city_Wattala  \n",
       "10493832.0                  0.0  \n",
       "10178643.0                  0.0  \n",
       "10513916.0                  1.0  \n",
       "10334589.0                  1.0  \n",
       "10458365.0                  0.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_f = train_data_f.astype('float64')\n",
    "train_data_f.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20b339dc",
   "metadata": {
    "papermill": {
     "duration": 0.015963,
     "end_time": "2024-05-17T05:47:21.708430",
     "exception": false,
     "start_time": "2024-05-17T05:47:21.692467",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Declare Independent and Dependent Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "168cc661",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:47:21.743292Z",
     "iopub.status.busy": "2024-05-17T05:47:21.742900Z",
     "iopub.status.idle": "2024-05-17T05:47:21.750202Z",
     "shell.execute_reply": "2024-05-17T05:47:21.749152Z"
    },
    "papermill": {
     "duration": 0.027684,
     "end_time": "2024-05-17T05:47:21.752406",
     "exception": false,
     "start_time": "2024-05-17T05:47:21.724722",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4., 1., 4., ..., 3., 3., 3.])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = train_data_f['cluster_catgeory'].to_numpy()\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2715e218",
   "metadata": {
    "papermill": {
     "duration": 0.015959,
     "end_time": "2024-05-17T05:47:21.784760",
     "exception": false,
     "start_time": "2024-05-17T05:47:21.768801",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Scale the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "aa90cb76",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:47:21.821753Z",
     "iopub.status.busy": "2024-05-17T05:47:21.821333Z",
     "iopub.status.idle": "2024-05-17T05:47:22.268671Z",
     "shell.execute_reply": "2024-05-17T05:47:22.267421Z"
    },
    "papermill": {
     "duration": 0.467332,
     "end_time": "2024-05-17T05:47:22.271198",
     "exception": false,
     "start_time": "2024-05-17T05:47:21.803866",
     "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>luxury_sales</th>\n",
       "      <th>fresh_sales</th>\n",
       "      <th>dry_sales</th>\n",
       "      <th>outlet_city_Batticaloa</th>\n",
       "      <th>outlet_city_Colombo</th>\n",
       "      <th>outlet_city_Dehiwala-Mount Lavinia</th>\n",
       "      <th>outlet_city_Galle</th>\n",
       "      <th>outlet_city_Gampaha</th>\n",
       "      <th>outlet_city_Homagama</th>\n",
       "      <th>outlet_city_Jaffna</th>\n",
       "      <th>...</th>\n",
       "      <th>outlet_city_Katunayake</th>\n",
       "      <th>outlet_city_Kelaniya</th>\n",
       "      <th>outlet_city_Kotte</th>\n",
       "      <th>outlet_city_Moratuwa</th>\n",
       "      <th>outlet_city_Negombo</th>\n",
       "      <th>outlet_city_Nuwara Eliya</th>\n",
       "      <th>outlet_city_Panadura</th>\n",
       "      <th>outlet_city_Peliyagoda</th>\n",
       "      <th>outlet_city_Trincomalee</th>\n",
       "      <th>outlet_city_Wattala</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10493832.0</th>\n",
       "      <td>-0.709602</td>\n",
       "      <td>-1.101678</td>\n",
       "      <td>0.180826</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10178643.0</th>\n",
       "      <td>-0.330909</td>\n",
       "      <td>-1.010483</td>\n",
       "      <td>0.390581</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10513916.0</th>\n",
       "      <td>0.364497</td>\n",
       "      <td>-0.884236</td>\n",
       "      <td>1.314052</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10334589.0</th>\n",
       "      <td>0.350624</td>\n",
       "      <td>-0.806674</td>\n",
       "      <td>1.297697</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10458365.0</th>\n",
       "      <td>0.420835</td>\n",
       "      <td>-0.707739</td>\n",
       "      <td>1.340002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10197979.0</th>\n",
       "      <td>1.961315</td>\n",
       "      <td>-0.160758</td>\n",
       "      <td>-0.359916</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10494575.0</th>\n",
       "      <td>4.153203</td>\n",
       "      <td>0.338460</td>\n",
       "      <td>0.469198</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10565682.0</th>\n",
       "      <td>3.183440</td>\n",
       "      <td>0.117181</td>\n",
       "      <td>-0.001753</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10351977.0</th>\n",
       "      <td>4.368067</td>\n",
       "      <td>0.564519</td>\n",
       "      <td>0.371035</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10637635.0</th>\n",
       "      <td>3.892102</td>\n",
       "      <td>0.479403</td>\n",
       "      <td>0.567796</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>771095 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            luxury_sales  fresh_sales  dry_sales  outlet_city_Batticaloa  \\\n",
       "10493832.0     -0.709602    -1.101678   0.180826                     0.0   \n",
       "10178643.0     -0.330909    -1.010483   0.390581                     0.0   \n",
       "10513916.0      0.364497    -0.884236   1.314052                     0.0   \n",
       "10334589.0      0.350624    -0.806674   1.297697                     0.0   \n",
       "10458365.0      0.420835    -0.707739   1.340002                     0.0   \n",
       "...                  ...          ...        ...                     ...   \n",
       "10197979.0      1.961315    -0.160758  -0.359916                     0.0   \n",
       "10494575.0      4.153203     0.338460   0.469198                     0.0   \n",
       "10565682.0      3.183440     0.117181  -0.001753                     0.0   \n",
       "10351977.0      4.368067     0.564519   0.371035                     0.0   \n",
       "10637635.0      3.892102     0.479403   0.567796                     0.0   \n",
       "\n",
       "            outlet_city_Colombo  outlet_city_Dehiwala-Mount Lavinia  \\\n",
       "10493832.0                  0.0                                 0.0   \n",
       "10178643.0                  0.0                                 0.0   \n",
       "10513916.0                  0.0                                 0.0   \n",
       "10334589.0                  0.0                                 0.0   \n",
       "10458365.0                  0.0                                 0.0   \n",
       "...                         ...                                 ...   \n",
       "10197979.0                  0.0                                 0.0   \n",
       "10494575.0                  0.0                                 0.0   \n",
       "10565682.0                  1.0                                 0.0   \n",
       "10351977.0                  0.0                                 0.0   \n",
       "10637635.0                  0.0                                 0.0   \n",
       "\n",
       "            outlet_city_Galle  outlet_city_Gampaha  outlet_city_Homagama  \\\n",
       "10493832.0                0.0                  0.0                   0.0   \n",
       "10178643.0                0.0                  0.0                   0.0   \n",
       "10513916.0                0.0                  0.0                   0.0   \n",
       "10334589.0                0.0                  0.0                   0.0   \n",
       "10458365.0                0.0                  0.0                   0.0   \n",
       "...                       ...                  ...                   ...   \n",
       "10197979.0                0.0                  1.0                   0.0   \n",
       "10494575.0                0.0                  0.0                   0.0   \n",
       "10565682.0                0.0                  0.0                   0.0   \n",
       "10351977.0                0.0                  0.0                   0.0   \n",
       "10637635.0                0.0                  0.0                   0.0   \n",
       "\n",
       "            outlet_city_Jaffna  ...  outlet_city_Katunayake  \\\n",
       "10493832.0                 0.0  ...                     0.0   \n",
       "10178643.0                 0.0  ...                     0.0   \n",
       "10513916.0                 0.0  ...                     0.0   \n",
       "10334589.0                 0.0  ...                     0.0   \n",
       "10458365.0                 0.0  ...                     0.0   \n",
       "...                        ...  ...                     ...   \n",
       "10197979.0                 0.0  ...                     0.0   \n",
       "10494575.0                 0.0  ...                     1.0   \n",
       "10565682.0                 0.0  ...                     0.0   \n",
       "10351977.0                 0.0  ...                     0.0   \n",
       "10637635.0                 0.0  ...                     0.0   \n",
       "\n",
       "            outlet_city_Kelaniya  outlet_city_Kotte  outlet_city_Moratuwa  \\\n",
       "10493832.0                   1.0                0.0                   0.0   \n",
       "10178643.0                   0.0                0.0                   1.0   \n",
       "10513916.0                   0.0                0.0                   0.0   \n",
       "10334589.0                   0.0                0.0                   0.0   \n",
       "10458365.0                   1.0                0.0                   0.0   \n",
       "...                          ...                ...                   ...   \n",
       "10197979.0                   0.0                0.0                   0.0   \n",
       "10494575.0                   0.0                0.0                   0.0   \n",
       "10565682.0                   0.0                0.0                   0.0   \n",
       "10351977.0                   0.0                0.0                   0.0   \n",
       "10637635.0                   0.0                0.0                   0.0   \n",
       "\n",
       "            outlet_city_Negombo  outlet_city_Nuwara Eliya  \\\n",
       "10493832.0                  0.0                       0.0   \n",
       "10178643.0                  0.0                       0.0   \n",
       "10513916.0                  0.0                       0.0   \n",
       "10334589.0                  0.0                       0.0   \n",
       "10458365.0                  0.0                       0.0   \n",
       "...                         ...                       ...   \n",
       "10197979.0                  0.0                       0.0   \n",
       "10494575.0                  0.0                       0.0   \n",
       "10565682.0                  0.0                       0.0   \n",
       "10351977.0                  0.0                       0.0   \n",
       "10637635.0                  1.0                       0.0   \n",
       "\n",
       "            outlet_city_Panadura  outlet_city_Peliyagoda  \\\n",
       "10493832.0                   0.0                     0.0   \n",
       "10178643.0                   0.0                     0.0   \n",
       "10513916.0                   0.0                     0.0   \n",
       "10334589.0                   0.0                     0.0   \n",
       "10458365.0                   0.0                     0.0   \n",
       "...                          ...                     ...   \n",
       "10197979.0                   0.0                     0.0   \n",
       "10494575.0                   0.0                     0.0   \n",
       "10565682.0                   0.0                     0.0   \n",
       "10351977.0                   0.0                     0.0   \n",
       "10637635.0                   0.0                     0.0   \n",
       "\n",
       "            outlet_city_Trincomalee  outlet_city_Wattala  \n",
       "10493832.0                      0.0                  0.0  \n",
       "10178643.0                      0.0                  0.0  \n",
       "10513916.0                      0.0                  1.0  \n",
       "10334589.0                      0.0                  1.0  \n",
       "10458365.0                      0.0                  0.0  \n",
       "...                             ...                  ...  \n",
       "10197979.0                      0.0                  0.0  \n",
       "10494575.0                      0.0                  0.0  \n",
       "10565682.0                      0.0                  0.0  \n",
       "10351977.0                      0.0                  0.0  \n",
       "10637635.0                      0.0                  0.0  \n",
       "\n",
       "[771095 rows x 23 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = train_data_f.drop('cluster_catgeory',axis=1)\n",
    "transform = preprocessing.StandardScaler()\n",
    "X[['luxury_sales','fresh_sales','dry_sales']] = transform.fit_transform(X[['luxury_sales','fresh_sales','dry_sales']])\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f721cf4",
   "metadata": {
    "papermill": {
     "duration": 0.016662,
     "end_time": "2024-05-17T05:47:22.304906",
     "exception": false,
     "start_time": "2024-05-17T05:47:22.288244",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Split the data or find model accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3cf83793",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:47:22.341330Z",
     "iopub.status.busy": "2024-05-17T05:47:22.340785Z",
     "iopub.status.idle": "2024-05-17T05:47:22.575430Z",
     "shell.execute_reply": "2024-05-17T05:47:22.574324Z"
    },
    "papermill": {
     "duration": 0.256318,
     "end_time": "2024-05-17T05:47:22.578093",
     "exception": false,
     "start_time": "2024-05-17T05:47:22.321775",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.2, random_state = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b273022",
   "metadata": {
    "papermill": {
     "duration": 0.01665,
     "end_time": "2024-05-17T05:47:22.613723",
     "exception": false,
     "start_time": "2024-05-17T05:47:22.597073",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### OneVsRest Multiclass Classification with Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c16aa440",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:47:22.650554Z",
     "iopub.status.busy": "2024-05-17T05:47:22.649284Z",
     "iopub.status.idle": "2024-05-17T05:47:48.624942Z",
     "shell.execute_reply": "2024-05-17T05:47:48.623697Z"
    },
    "papermill": {
     "duration": 25.997908,
     "end_time": "2024-05-17T05:47:48.628693",
     "exception": false,
     "start_time": "2024-05-17T05:47:22.630785",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsRestClassifier(estimator=LogisticRegression())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneVsRestClassifier</label><div class=\"sk-toggleable__content\"><pre>OneVsRestClassifier(estimator=LogisticRegression())</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "OneVsRestClassifier(estimator=LogisticRegression())"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "ovr = OneVsRestClassifier(lr)\n",
    "ovr.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a1d1f2ab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:47:48.693128Z",
     "iopub.status.busy": "2024-05-17T05:47:48.692566Z",
     "iopub.status.idle": "2024-05-17T05:47:48.827703Z",
     "shell.execute_reply": "2024-05-17T05:47:48.826413Z"
    },
    "papermill": {
     "duration": 0.171994,
     "end_time": "2024-05-17T05:47:48.831397",
     "exception": false,
     "start_time": "2024-05-17T05:47:48.659403",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "Y_ovr = ovr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "15ada44b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:47:48.892305Z",
     "iopub.status.busy": "2024-05-17T05:47:48.891781Z",
     "iopub.status.idle": "2024-05-17T05:47:48.926714Z",
     "shell.execute_reply": "2024-05-17T05:47:48.925551Z"
    },
    "papermill": {
     "duration": 0.069022,
     "end_time": "2024-05-17T05:47:48.930078",
     "exception": false,
     "start_time": "2024-05-17T05:47:48.861056",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9997989871546308"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracyOvr = accuracy_score(Y_test,Y_ovr)\n",
    "accuracyOvr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "407b35d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:47:48.972440Z",
     "iopub.status.busy": "2024-05-17T05:47:48.972052Z",
     "iopub.status.idle": "2024-05-17T05:47:49.382551Z",
     "shell.execute_reply": "2024-05-17T05:47:49.381212Z"
    },
    "papermill": {
     "duration": 0.43274,
     "end_time": "2024-05-17T05:47:49.385951",
     "exception": false,
     "start_time": "2024-05-17T05:47:48.953211",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         1.0       1.00      1.00      1.00     37801\n",
      "         2.0       1.00      1.00      1.00     30637\n",
      "         3.0       1.00      1.00      1.00      9960\n",
      "         4.0       1.00      1.00      1.00     34229\n",
      "         5.0       1.00      1.00      1.00      7840\n",
      "         6.0       1.00      1.00      1.00     33752\n",
      "\n",
      "    accuracy                           1.00    154219\n",
      "   macro avg       1.00      1.00      1.00    154219\n",
      "weighted avg       1.00      1.00      1.00    154219\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Y_test,Y_ovr))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "988ae39c",
   "metadata": {
    "papermill": {
     "duration": 0.018227,
     "end_time": "2024-05-17T05:47:49.422729",
     "exception": false,
     "start_time": "2024-05-17T05:47:49.404502",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### OneVsOne Multiclass Classification with SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9de5bfd1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:47:49.461359Z",
     "iopub.status.busy": "2024-05-17T05:47:49.460924Z",
     "iopub.status.idle": "2024-05-17T05:48:42.004555Z",
     "shell.execute_reply": "2024-05-17T05:48:42.003141Z"
    },
    "papermill": {
     "duration": 52.583581,
     "end_time": "2024-05-17T05:48:42.024700",
     "exception": false,
     "start_time": "2024-05-17T05:47:49.441119",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsOneClassifier(estimator=SVC())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneVsOneClassifier</label><div class=\"sk-toggleable__content\"><pre>OneVsOneClassifier(estimator=SVC())</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "OneVsOneClassifier(estimator=SVC())"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm = SVC()\n",
    "ovo = OneVsOneClassifier(svm)\n",
    "ovo.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fece2ff2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:48:42.065735Z",
     "iopub.status.busy": "2024-05-17T05:48:42.064744Z",
     "iopub.status.idle": "2024-05-17T05:49:32.796530Z",
     "shell.execute_reply": "2024-05-17T05:49:32.795173Z"
    },
    "papermill": {
     "duration": 50.755894,
     "end_time": "2024-05-17T05:49:32.799839",
     "exception": false,
     "start_time": "2024-05-17T05:48:42.043945",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "Y_ovo = ovo.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b397bdeb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:49:32.840997Z",
     "iopub.status.busy": "2024-05-17T05:49:32.840584Z",
     "iopub.status.idle": "2024-05-17T05:49:32.860913Z",
     "shell.execute_reply": "2024-05-17T05:49:32.859702Z"
    },
    "papermill": {
     "duration": 0.043632,
     "end_time": "2024-05-17T05:49:32.863617",
     "exception": false,
     "start_time": "2024-05-17T05:49:32.819985",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9997989871546308"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracyOvo = accuracy_score(Y_test,Y_ovo)\n",
    "accuracyOvo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "049f188b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:49:32.902331Z",
     "iopub.status.busy": "2024-05-17T05:49:32.901914Z",
     "iopub.status.idle": "2024-05-17T05:49:33.328158Z",
     "shell.execute_reply": "2024-05-17T05:49:33.326676Z"
    },
    "papermill": {
     "duration": 0.448898,
     "end_time": "2024-05-17T05:49:33.331335",
     "exception": false,
     "start_time": "2024-05-17T05:49:32.882437",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         1.0       1.00      1.00      1.00     37801\n",
      "         2.0       1.00      1.00      1.00     30637\n",
      "         3.0       1.00      1.00      1.00      9960\n",
      "         4.0       1.00      1.00      1.00     34229\n",
      "         5.0       1.00      1.00      1.00      7840\n",
      "         6.0       1.00      1.00      1.00     33752\n",
      "\n",
      "    accuracy                           1.00    154219\n",
      "   macro avg       1.00      1.00      1.00    154219\n",
      "weighted avg       1.00      1.00      1.00    154219\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Y_test,Y_ovo))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "472585bf",
   "metadata": {
    "papermill": {
     "duration": 0.017828,
     "end_time": "2024-05-17T05:49:33.368618",
     "exception": false,
     "start_time": "2024-05-17T05:49:33.350790",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Both methods gave same accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a08be499",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:49:33.408510Z",
     "iopub.status.busy": "2024-05-17T05:49:33.408134Z",
     "iopub.status.idle": "2024-05-17T05:49:33.649742Z",
     "shell.execute_reply": "2024-05-17T05:49:33.648317Z"
    },
    "papermill": {
     "duration": 0.264929,
     "end_time": "2024-05-17T05:49:33.652286",
     "exception": false,
     "start_time": "2024-05-17T05:49:33.387357",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  order = pd.unique(vector)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracies = [accuracyOvr,accuracyOvo]\n",
    "models = ['OneVsRest','OnevsOne']\n",
    "\n",
    "ax = sns.barplot(x=models,y=accuracies)\n",
    "plt.xticks(rotation=60)\n",
    "plt.ylim(0.99929,1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dbe47c8",
   "metadata": {
    "papermill": {
     "duration": 0.018327,
     "end_time": "2024-05-17T05:49:33.689974",
     "exception": false,
     "start_time": "2024-05-17T05:49:33.671647",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Step 5: Load New Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7273d531",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:49:33.728743Z",
     "iopub.status.busy": "2024-05-17T05:49:33.728325Z",
     "iopub.status.idle": "2024-05-17T05:49:33.842709Z",
     "shell.execute_reply": "2024-05-17T05:49:33.840930Z"
    },
    "papermill": {
     "duration": 0.137447,
     "end_time": "2024-05-17T05:49:33.845902",
     "exception": false,
     "start_time": "2024-05-17T05:49:33.708455",
     "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>Customer_ID</th>\n",
       "      <th>outlet_city</th>\n",
       "      <th>luxury_sales</th>\n",
       "      <th>fresh_sales</th>\n",
       "      <th>dry_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33574</td>\n",
       "      <td>batticaloa</td>\n",
       "      <td>2686.5</td>\n",
       "      <td>3582</td>\n",
       "      <td>12537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10089</td>\n",
       "      <td>batticaloa</td>\n",
       "      <td>1717.56</td>\n",
       "      <td>2576.34</td>\n",
       "      <td>9446.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38329</td>\n",
       "      <td>batticaloa</td>\n",
       "      <td>854.04</td>\n",
       "      <td>1242.24</td>\n",
       "      <td>5201.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11376</td>\n",
       "      <td>batticaloa</td>\n",
       "      <td>1638.12</td>\n",
       "      <td>2320.67</td>\n",
       "      <td>9282.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12410</td>\n",
       "      <td>batticaloa</td>\n",
       "      <td>1039.09</td>\n",
       "      <td>1518.67</td>\n",
       "      <td>5435.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer_ID outlet_city luxury_sales fresh_sales dry_sales\n",
       "0        33574  batticaloa       2686.5        3582     12537\n",
       "1        10089  batticaloa      1717.56     2576.34   9446.58\n",
       "2        38329  batticaloa       854.04     1242.24   5201.88\n",
       "3        11376  batticaloa      1638.12     2320.67   9282.68\n",
       "4        12410  batticaloa      1039.09     1518.67   5435.24"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data = pd.read_csv('/kaggle/input/data-storm-5-0/test_kaggle.csv')\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9929adb8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:49:33.891290Z",
     "iopub.status.busy": "2024-05-17T05:49:33.890783Z",
     "iopub.status.idle": "2024-05-17T05:49:33.897841Z",
     "shell.execute_reply": "2024-05-17T05:49:33.896295Z"
    },
    "papermill": {
     "duration": 0.03334,
     "end_time": "2024-05-17T05:49:33.900584",
     "exception": false,
     "start_time": "2024-05-17T05:49:33.867244",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_data.set_index('Customer_ID',inplace=True)\n",
    "test_data.index.name = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81b42c86",
   "metadata": {
    "papermill": {
     "duration": 0.020472,
     "end_time": "2024-05-17T05:49:33.941046",
     "exception": false,
     "start_time": "2024-05-17T05:49:33.920574",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Handle Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6b7485c4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:49:33.985814Z",
     "iopub.status.busy": "2024-05-17T05:49:33.984749Z",
     "iopub.status.idle": "2024-05-17T05:49:34.049472Z",
     "shell.execute_reply": "2024-05-17T05:49:34.047734Z"
    },
    "papermill": {
     "duration": 0.091649,
     "end_time": "2024-05-17T05:49:34.052906",
     "exception": false,
     "start_time": "2024-05-17T05:49:33.961257",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_data['luxury_sales'] = pd.to_numeric(test_data['luxury_sales'], errors='coerce')\n",
    "test_data['fresh_sales'] = pd.to_numeric(test_data['fresh_sales'], errors='coerce')\n",
    "test_data['dry_sales'] = pd.to_numeric(test_data['dry_sales'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e0a796f4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:49:34.097730Z",
     "iopub.status.busy": "2024-05-17T05:49:34.097194Z",
     "iopub.status.idle": "2024-05-17T05:49:34.109819Z",
     "shell.execute_reply": "2024-05-17T05:49:34.108702Z"
    },
    "papermill": {
     "duration": 0.038314,
     "end_time": "2024-05-17T05:49:34.112784",
     "exception": false,
     "start_time": "2024-05-17T05:49:34.074470",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "outlet_city     0\n",
       "luxury_sales    2\n",
       "fresh_sales     1\n",
       "dry_sales       1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa756cce",
   "metadata": {
    "papermill": {
     "duration": 0.020238,
     "end_time": "2024-05-17T05:49:34.153470",
     "exception": false,
     "start_time": "2024-05-17T05:49:34.133232",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Used the mean to fill missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "64a43e64",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:49:34.200710Z",
     "iopub.status.busy": "2024-05-17T05:49:34.200167Z",
     "iopub.status.idle": "2024-05-17T05:49:34.211341Z",
     "shell.execute_reply": "2024-05-17T05:49:34.210012Z"
    },
    "papermill": {
     "duration": 0.037533,
     "end_time": "2024-05-17T05:49:34.214202",
     "exception": false,
     "start_time": "2024-05-17T05:49:34.176669",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_data['luxury_sales'] = test_data['luxury_sales'].fillna(value=test_data['luxury_sales'].mean())\n",
    "test_data['fresh_sales'] = test_data['fresh_sales'].fillna(value=test_data['fresh_sales'].mean()) \n",
    "test_data['dry_sales'] = test_data['dry_sales'].fillna(value=test_data['dry_sales'].mean()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "77133f5b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:49:34.256898Z",
     "iopub.status.busy": "2024-05-17T05:49:34.256342Z",
     "iopub.status.idle": "2024-05-17T05:49:34.270195Z",
     "shell.execute_reply": "2024-05-17T05:49:34.269008Z"
    },
    "papermill": {
     "duration": 0.038863,
     "end_time": "2024-05-17T05:49:34.273026",
     "exception": false,
     "start_time": "2024-05-17T05:49:34.234163",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "outlet_city     0\n",
       "luxury_sales    0\n",
       "fresh_sales     0\n",
       "dry_sales       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2234219",
   "metadata": {
    "papermill": {
     "duration": 0.02197,
     "end_time": "2024-05-17T05:49:34.317666",
     "exception": false,
     "start_time": "2024-05-17T05:49:34.295696",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Handle duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "05004320",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:49:34.365435Z",
     "iopub.status.busy": "2024-05-17T05:49:34.364682Z",
     "iopub.status.idle": "2024-05-17T05:49:34.435346Z",
     "shell.execute_reply": "2024-05-17T05:49:34.433719Z"
    },
    "papermill": {
     "duration": 0.098076,
     "end_time": "2024-05-17T05:49:34.438121",
     "exception": false,
     "start_time": "2024-05-17T05:49:34.340045",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "outlet_city\n",
       "Batticaloa                40866\n",
       "Colombo                   40889\n",
       "Dehiwala-Mount Lavinia    36675\n",
       "Galle                     40884\n",
       "Gampaha                   40116\n",
       "Homagama                  36478\n",
       "Jaffna                    40548\n",
       "Kaduwela                  36502\n",
       "Kalmunai                  40590\n",
       "Kandy                     40393\n",
       "Katunayake                40518\n",
       "Kelaniya                  35818\n",
       "Kotte                     36633\n",
       "Moratuwa                  36247\n",
       "Negombo                   40558\n",
       "Nuwara Eliya              36926\n",
       "Panadura                  36552\n",
       "Peliyagoda                36986\n",
       "Trincomalee               40398\n",
       "Wattala                   36518\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['outlet_city'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "47f33b2f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:49:34.485835Z",
     "iopub.status.busy": "2024-05-17T05:49:34.485300Z",
     "iopub.status.idle": "2024-05-17T05:49:34.499728Z",
     "shell.execute_reply": "2024-05-17T05:49:34.498212Z"
    },
    "papermill": {
     "duration": 0.041738,
     "end_time": "2024-05-17T05:49:34.502943",
     "exception": false,
     "start_time": "2024-05-17T05:49:34.461205",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "outlet_city\n",
       "Anuradhapura              1645\n",
       "Batticaloa                1671\n",
       "Colombo                   2092\n",
       "Dehiwala-Mount Lavinia    1981\n",
       "Galle                      456\n",
       "Gampaha                   2198\n",
       "Homagama                  1930\n",
       "Jaffna                    2226\n",
       "Kaduwela                  1943\n",
       "Kalmunai                  1634\n",
       "Kandy                     2101\n",
       "Katunayake                2157\n",
       "Kelaniya                  1934\n",
       "Kotte                      379\n",
       "Madawachiya               1499\n",
       "MoraTuwa                   340\n",
       "Moratuwa                  1563\n",
       "Negombo                   2129\n",
       "Nuwara Eliya              1963\n",
       "Panadura                  1810\n",
       "PeliyagodA                 202\n",
       "Peliyagoda                1839\n",
       "Trincomale                1814\n",
       "Trincomalee                258\n",
       "Wattala                   1938\n",
       "batticaloa                 448\n",
       "kalmunai                   599\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data['outlet_city'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4cef3bda",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:49:34.548518Z",
     "iopub.status.busy": "2024-05-17T05:49:34.547311Z",
     "iopub.status.idle": "2024-05-17T05:49:34.579091Z",
     "shell.execute_reply": "2024-05-17T05:49:34.577956Z"
    },
    "papermill": {
     "duration": 0.057095,
     "end_time": "2024-05-17T05:49:34.581867",
     "exception": false,
     "start_time": "2024-05-17T05:49:34.524772",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_data['outlet_city'] = test_data['outlet_city'].replace('batticaloa','Batticaloa')\n",
    "test_data['outlet_city'] = test_data['outlet_city'].replace('MoraTuwa','Moratuwa')\n",
    "test_data['outlet_city'] = test_data['outlet_city'].replace('PeliyagodA','Peliyagoda')\n",
    "test_data['outlet_city'] = test_data['outlet_city'].replace('kalmunai','Kalmunai')\n",
    "test_data['outlet_city'] = test_data['outlet_city'].replace('Trincomale','Trincomalee')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8b46dbf9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:49:34.626232Z",
     "iopub.status.busy": "2024-05-17T05:49:34.625725Z",
     "iopub.status.idle": "2024-05-17T05:49:34.639518Z",
     "shell.execute_reply": "2024-05-17T05:49:34.638069Z"
    },
    "papermill": {
     "duration": 0.039801,
     "end_time": "2024-05-17T05:49:34.642208",
     "exception": false,
     "start_time": "2024-05-17T05:49:34.602407",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "outlet_city\n",
       "Anuradhapura              1645\n",
       "Batticaloa                2119\n",
       "Colombo                   2092\n",
       "Dehiwala-Mount Lavinia    1981\n",
       "Galle                      456\n",
       "Gampaha                   2198\n",
       "Homagama                  1930\n",
       "Jaffna                    2226\n",
       "Kaduwela                  1943\n",
       "Kalmunai                  2233\n",
       "Kandy                     2101\n",
       "Katunayake                2157\n",
       "Kelaniya                  1934\n",
       "Kotte                      379\n",
       "Madawachiya               1499\n",
       "Moratuwa                  1903\n",
       "Negombo                   2129\n",
       "Nuwara Eliya              1963\n",
       "Panadura                  1810\n",
       "Peliyagoda                2041\n",
       "Trincomalee               2072\n",
       "Wattala                   1938\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data['outlet_city'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cd91131",
   "metadata": {
    "papermill": {
     "duration": 0.020346,
     "end_time": "2024-05-17T05:49:34.684404",
     "exception": false,
     "start_time": "2024-05-17T05:49:34.664058",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d443a6ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:49:34.728747Z",
     "iopub.status.busy": "2024-05-17T05:49:34.728169Z",
     "iopub.status.idle": "2024-05-17T05:49:34.764490Z",
     "shell.execute_reply": "2024-05-17T05:49:34.763228Z"
    },
    "papermill": {
     "duration": 0.06241,
     "end_time": "2024-05-17T05:49:34.767288",
     "exception": false,
     "start_time": "2024-05-17T05:49:34.704878",
     "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>luxury_sales</th>\n",
       "      <th>fresh_sales</th>\n",
       "      <th>dry_sales</th>\n",
       "      <th>outlet_city_Anuradhapura</th>\n",
       "      <th>outlet_city_Batticaloa</th>\n",
       "      <th>outlet_city_Colombo</th>\n",
       "      <th>outlet_city_Dehiwala-Mount Lavinia</th>\n",
       "      <th>outlet_city_Galle</th>\n",
       "      <th>outlet_city_Gampaha</th>\n",
       "      <th>outlet_city_Homagama</th>\n",
       "      <th>...</th>\n",
       "      <th>outlet_city_Kelaniya</th>\n",
       "      <th>outlet_city_Kotte</th>\n",
       "      <th>outlet_city_Madawachiya</th>\n",
       "      <th>outlet_city_Moratuwa</th>\n",
       "      <th>outlet_city_Negombo</th>\n",
       "      <th>outlet_city_Nuwara Eliya</th>\n",
       "      <th>outlet_city_Panadura</th>\n",
       "      <th>outlet_city_Peliyagoda</th>\n",
       "      <th>outlet_city_Trincomalee</th>\n",
       "      <th>outlet_city_Wattala</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33574</th>\n",
       "      <td>2686.50</td>\n",
       "      <td>3582.00</td>\n",
       "      <td>12537.00</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10089</th>\n",
       "      <td>1717.56</td>\n",
       "      <td>2576.34</td>\n",
       "      <td>9446.58</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38329</th>\n",
       "      <td>854.04</td>\n",
       "      <td>1242.24</td>\n",
       "      <td>5201.88</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11376</th>\n",
       "      <td>1638.12</td>\n",
       "      <td>2320.67</td>\n",
       "      <td>9282.68</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12410</th>\n",
       "      <td>1039.09</td>\n",
       "      <td>1518.67</td>\n",
       "      <td>5435.24</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       luxury_sales  fresh_sales  dry_sales  outlet_city_Anuradhapura  \\\n",
       "33574       2686.50      3582.00   12537.00                     False   \n",
       "10089       1717.56      2576.34    9446.58                     False   \n",
       "38329        854.04      1242.24    5201.88                     False   \n",
       "11376       1638.12      2320.67    9282.68                     False   \n",
       "12410       1039.09      1518.67    5435.24                     False   \n",
       "\n",
       "       outlet_city_Batticaloa  outlet_city_Colombo  \\\n",
       "33574                    True                False   \n",
       "10089                    True                False   \n",
       "38329                    True                False   \n",
       "11376                    True                False   \n",
       "12410                    True                False   \n",
       "\n",
       "       outlet_city_Dehiwala-Mount Lavinia  outlet_city_Galle  \\\n",
       "33574                               False              False   \n",
       "10089                               False              False   \n",
       "38329                               False              False   \n",
       "11376                               False              False   \n",
       "12410                               False              False   \n",
       "\n",
       "       outlet_city_Gampaha  outlet_city_Homagama  ...  outlet_city_Kelaniya  \\\n",
       "33574                False                 False  ...                 False   \n",
       "10089                False                 False  ...                 False   \n",
       "38329                False                 False  ...                 False   \n",
       "11376                False                 False  ...                 False   \n",
       "12410                False                 False  ...                 False   \n",
       "\n",
       "       outlet_city_Kotte  outlet_city_Madawachiya  outlet_city_Moratuwa  \\\n",
       "33574              False                    False                 False   \n",
       "10089              False                    False                 False   \n",
       "38329              False                    False                 False   \n",
       "11376              False                    False                 False   \n",
       "12410              False                    False                 False   \n",
       "\n",
       "       outlet_city_Negombo  outlet_city_Nuwara Eliya  outlet_city_Panadura  \\\n",
       "33574                False                     False                 False   \n",
       "10089                False                     False                 False   \n",
       "38329                False                     False                 False   \n",
       "11376                False                     False                 False   \n",
       "12410                False                     False                 False   \n",
       "\n",
       "       outlet_city_Peliyagoda  outlet_city_Trincomalee  outlet_city_Wattala  \n",
       "33574                   False                    False                False  \n",
       "10089                   False                    False                False  \n",
       "38329                   False                    False                False  \n",
       "11376                   False                    False                False  \n",
       "12410                   False                    False                False  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_f = pd.get_dummies(test_data)\n",
    "test_data_f.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "552602e4",
   "metadata": {
    "papermill": {
     "duration": 0.020967,
     "end_time": "2024-05-17T05:49:34.808853",
     "exception": false,
     "start_time": "2024-05-17T05:49:34.787886",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Drop Unseen features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ee6eae03",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:49:34.853666Z",
     "iopub.status.busy": "2024-05-17T05:49:34.853198Z",
     "iopub.status.idle": "2024-05-17T05:49:34.881369Z",
     "shell.execute_reply": "2024-05-17T05:49:34.880153Z"
    },
    "papermill": {
     "duration": 0.054094,
     "end_time": "2024-05-17T05:49:34.883920",
     "exception": false,
     "start_time": "2024-05-17T05:49:34.829826",
     "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>luxury_sales</th>\n",
       "      <th>fresh_sales</th>\n",
       "      <th>dry_sales</th>\n",
       "      <th>outlet_city_Batticaloa</th>\n",
       "      <th>outlet_city_Colombo</th>\n",
       "      <th>outlet_city_Dehiwala-Mount Lavinia</th>\n",
       "      <th>outlet_city_Galle</th>\n",
       "      <th>outlet_city_Gampaha</th>\n",
       "      <th>outlet_city_Homagama</th>\n",
       "      <th>outlet_city_Jaffna</th>\n",
       "      <th>...</th>\n",
       "      <th>outlet_city_Katunayake</th>\n",
       "      <th>outlet_city_Kelaniya</th>\n",
       "      <th>outlet_city_Kotte</th>\n",
       "      <th>outlet_city_Moratuwa</th>\n",
       "      <th>outlet_city_Negombo</th>\n",
       "      <th>outlet_city_Nuwara Eliya</th>\n",
       "      <th>outlet_city_Panadura</th>\n",
       "      <th>outlet_city_Peliyagoda</th>\n",
       "      <th>outlet_city_Trincomalee</th>\n",
       "      <th>outlet_city_Wattala</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33574</th>\n",
       "      <td>2686.50</td>\n",
       "      <td>3582.00</td>\n",
       "      <td>12537.00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10089</th>\n",
       "      <td>1717.56</td>\n",
       "      <td>2576.34</td>\n",
       "      <td>9446.58</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38329</th>\n",
       "      <td>854.04</td>\n",
       "      <td>1242.24</td>\n",
       "      <td>5201.88</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11376</th>\n",
       "      <td>1638.12</td>\n",
       "      <td>2320.67</td>\n",
       "      <td>9282.68</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12410</th>\n",
       "      <td>1039.09</td>\n",
       "      <td>1518.67</td>\n",
       "      <td>5435.24</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       luxury_sales  fresh_sales  dry_sales  outlet_city_Batticaloa  \\\n",
       "33574       2686.50      3582.00   12537.00                    True   \n",
       "10089       1717.56      2576.34    9446.58                    True   \n",
       "38329        854.04      1242.24    5201.88                    True   \n",
       "11376       1638.12      2320.67    9282.68                    True   \n",
       "12410       1039.09      1518.67    5435.24                    True   \n",
       "\n",
       "       outlet_city_Colombo  outlet_city_Dehiwala-Mount Lavinia  \\\n",
       "33574                False                               False   \n",
       "10089                False                               False   \n",
       "38329                False                               False   \n",
       "11376                False                               False   \n",
       "12410                False                               False   \n",
       "\n",
       "       outlet_city_Galle  outlet_city_Gampaha  outlet_city_Homagama  \\\n",
       "33574              False                False                 False   \n",
       "10089              False                False                 False   \n",
       "38329              False                False                 False   \n",
       "11376              False                False                 False   \n",
       "12410              False                False                 False   \n",
       "\n",
       "       outlet_city_Jaffna  ...  outlet_city_Katunayake  outlet_city_Kelaniya  \\\n",
       "33574               False  ...                   False                 False   \n",
       "10089               False  ...                   False                 False   \n",
       "38329               False  ...                   False                 False   \n",
       "11376               False  ...                   False                 False   \n",
       "12410               False  ...                   False                 False   \n",
       "\n",
       "       outlet_city_Kotte  outlet_city_Moratuwa  outlet_city_Negombo  \\\n",
       "33574              False                 False                False   \n",
       "10089              False                 False                False   \n",
       "38329              False                 False                False   \n",
       "11376              False                 False                False   \n",
       "12410              False                 False                False   \n",
       "\n",
       "       outlet_city_Nuwara Eliya  outlet_city_Panadura  outlet_city_Peliyagoda  \\\n",
       "33574                     False                 False                   False   \n",
       "10089                     False                 False                   False   \n",
       "38329                     False                 False                   False   \n",
       "11376                     False                 False                   False   \n",
       "12410                     False                 False                   False   \n",
       "\n",
       "       outlet_city_Trincomalee  outlet_city_Wattala  \n",
       "33574                    False                False  \n",
       "10089                    False                False  \n",
       "38329                    False                False  \n",
       "11376                    False                False  \n",
       "12410                    False                False  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new = test_data_f.drop(['outlet_city_Anuradhapura','outlet_city_Madawachiya'],axis=1)\n",
    "X_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9d4c3a4a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:49:34.926868Z",
     "iopub.status.busy": "2024-05-17T05:49:34.926450Z",
     "iopub.status.idle": "2024-05-17T05:49:34.933932Z",
     "shell.execute_reply": "2024-05-17T05:49:34.931960Z"
    },
    "papermill": {
     "duration": 0.03178,
     "end_time": "2024-05-17T05:49:34.936754",
     "exception": false,
     "start_time": "2024-05-17T05:49:34.904974",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_new = X_new.astype('float64')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20aaeada",
   "metadata": {
    "papermill": {
     "duration": 0.020079,
     "end_time": "2024-05-17T05:49:34.977884",
     "exception": false,
     "start_time": "2024-05-17T05:49:34.957805",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Scaling variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e5c53d9f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:49:35.021910Z",
     "iopub.status.busy": "2024-05-17T05:49:35.021506Z",
     "iopub.status.idle": "2024-05-17T05:49:35.084285Z",
     "shell.execute_reply": "2024-05-17T05:49:35.083107Z"
    },
    "papermill": {
     "duration": 0.089804,
     "end_time": "2024-05-17T05:49:35.087692",
     "exception": false,
     "start_time": "2024-05-17T05:49:34.997888",
     "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>luxury_sales</th>\n",
       "      <th>fresh_sales</th>\n",
       "      <th>dry_sales</th>\n",
       "      <th>outlet_city_Batticaloa</th>\n",
       "      <th>outlet_city_Colombo</th>\n",
       "      <th>outlet_city_Dehiwala-Mount Lavinia</th>\n",
       "      <th>outlet_city_Galle</th>\n",
       "      <th>outlet_city_Gampaha</th>\n",
       "      <th>outlet_city_Homagama</th>\n",
       "      <th>outlet_city_Jaffna</th>\n",
       "      <th>...</th>\n",
       "      <th>outlet_city_Katunayake</th>\n",
       "      <th>outlet_city_Kelaniya</th>\n",
       "      <th>outlet_city_Kotte</th>\n",
       "      <th>outlet_city_Moratuwa</th>\n",
       "      <th>outlet_city_Negombo</th>\n",
       "      <th>outlet_city_Nuwara Eliya</th>\n",
       "      <th>outlet_city_Panadura</th>\n",
       "      <th>outlet_city_Peliyagoda</th>\n",
       "      <th>outlet_city_Trincomalee</th>\n",
       "      <th>outlet_city_Wattala</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33574</th>\n",
       "      <td>0.756537</td>\n",
       "      <td>-0.257025</td>\n",
       "      <td>2.300092</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10089</th>\n",
       "      <td>-0.208780</td>\n",
       "      <td>-0.557282</td>\n",
       "      <td>1.394059</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38329</th>\n",
       "      <td>-1.069072</td>\n",
       "      <td>-0.955602</td>\n",
       "      <td>0.149621</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11376</th>\n",
       "      <td>-0.287923</td>\n",
       "      <td>-0.633617</td>\n",
       "      <td>1.346008</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12410</th>\n",
       "      <td>-0.884714</td>\n",
       "      <td>-0.873069</td>\n",
       "      <td>0.218036</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33698</th>\n",
       "      <td>1.981918</td>\n",
       "      <td>-0.084076</td>\n",
       "      <td>-0.227226</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4185</th>\n",
       "      <td>1.260067</td>\n",
       "      <td>-0.429549</td>\n",
       "      <td>-0.467172</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28664</th>\n",
       "      <td>1.904329</td>\n",
       "      <td>-0.146704</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11874</th>\n",
       "      <td>2.377713</td>\n",
       "      <td>-0.077573</td>\n",
       "      <td>-0.110754</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11431</th>\n",
       "      <td>2.276492</td>\n",
       "      <td>-0.068879</td>\n",
       "      <td>-0.140540</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>40749 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       luxury_sales  fresh_sales  dry_sales  outlet_city_Batticaloa  \\\n",
       "33574      0.756537    -0.257025   2.300092                     1.0   \n",
       "10089     -0.208780    -0.557282   1.394059                     1.0   \n",
       "38329     -1.069072    -0.955602   0.149621                     1.0   \n",
       "11376     -0.287923    -0.633617   1.346008                     1.0   \n",
       "12410     -0.884714    -0.873069   0.218036                     1.0   \n",
       "...             ...          ...        ...                     ...   \n",
       "33698      1.981918    -0.084076  -0.227226                     0.0   \n",
       "4185       1.260067    -0.429549  -0.467172                     0.0   \n",
       "28664      1.904329    -0.146704   0.000000                     0.0   \n",
       "11874      2.377713    -0.077573  -0.110754                     0.0   \n",
       "11431      2.276492    -0.068879  -0.140540                     0.0   \n",
       "\n",
       "       outlet_city_Colombo  outlet_city_Dehiwala-Mount Lavinia  \\\n",
       "33574                  0.0                                 0.0   \n",
       "10089                  0.0                                 0.0   \n",
       "38329                  0.0                                 0.0   \n",
       "11376                  0.0                                 0.0   \n",
       "12410                  0.0                                 0.0   \n",
       "...                    ...                                 ...   \n",
       "33698                  0.0                                 0.0   \n",
       "4185                   0.0                                 0.0   \n",
       "28664                  0.0                                 0.0   \n",
       "11874                  0.0                                 0.0   \n",
       "11431                  0.0                                 0.0   \n",
       "\n",
       "       outlet_city_Galle  outlet_city_Gampaha  outlet_city_Homagama  \\\n",
       "33574                0.0                  0.0                   0.0   \n",
       "10089                0.0                  0.0                   0.0   \n",
       "38329                0.0                  0.0                   0.0   \n",
       "11376                0.0                  0.0                   0.0   \n",
       "12410                0.0                  0.0                   0.0   \n",
       "...                  ...                  ...                   ...   \n",
       "33698                0.0                  0.0                   0.0   \n",
       "4185                 0.0                  0.0                   0.0   \n",
       "28664                0.0                  0.0                   0.0   \n",
       "11874                0.0                  0.0                   0.0   \n",
       "11431                0.0                  0.0                   0.0   \n",
       "\n",
       "       outlet_city_Jaffna  ...  outlet_city_Katunayake  outlet_city_Kelaniya  \\\n",
       "33574                 0.0  ...                     0.0                   0.0   \n",
       "10089                 0.0  ...                     0.0                   0.0   \n",
       "38329                 0.0  ...                     0.0                   0.0   \n",
       "11376                 0.0  ...                     0.0                   0.0   \n",
       "12410                 0.0  ...                     0.0                   0.0   \n",
       "...                   ...  ...                     ...                   ...   \n",
       "33698                 0.0  ...                     0.0                   0.0   \n",
       "4185                  0.0  ...                     0.0                   0.0   \n",
       "28664                 0.0  ...                     0.0                   0.0   \n",
       "11874                 0.0  ...                     0.0                   0.0   \n",
       "11431                 0.0  ...                     0.0                   0.0   \n",
       "\n",
       "       outlet_city_Kotte  outlet_city_Moratuwa  outlet_city_Negombo  \\\n",
       "33574                0.0                   0.0                  0.0   \n",
       "10089                0.0                   0.0                  0.0   \n",
       "38329                0.0                   0.0                  0.0   \n",
       "11376                0.0                   0.0                  0.0   \n",
       "12410                0.0                   0.0                  0.0   \n",
       "...                  ...                   ...                  ...   \n",
       "33698                0.0                   0.0                  0.0   \n",
       "4185                 0.0                   0.0                  0.0   \n",
       "28664                0.0                   0.0                  0.0   \n",
       "11874                0.0                   0.0                  0.0   \n",
       "11431                0.0                   0.0                  0.0   \n",
       "\n",
       "       outlet_city_Nuwara Eliya  outlet_city_Panadura  outlet_city_Peliyagoda  \\\n",
       "33574                       0.0                   0.0                     0.0   \n",
       "10089                       0.0                   0.0                     0.0   \n",
       "38329                       0.0                   0.0                     0.0   \n",
       "11376                       0.0                   0.0                     0.0   \n",
       "12410                       0.0                   0.0                     0.0   \n",
       "...                         ...                   ...                     ...   \n",
       "33698                       0.0                   0.0                     0.0   \n",
       "4185                        0.0                   0.0                     0.0   \n",
       "28664                       0.0                   0.0                     0.0   \n",
       "11874                       0.0                   0.0                     0.0   \n",
       "11431                       0.0                   0.0                     0.0   \n",
       "\n",
       "       outlet_city_Trincomalee  outlet_city_Wattala  \n",
       "33574                      0.0                  0.0  \n",
       "10089                      0.0                  0.0  \n",
       "38329                      0.0                  0.0  \n",
       "11376                      0.0                  0.0  \n",
       "12410                      0.0                  0.0  \n",
       "...                        ...                  ...  \n",
       "33698                      0.0                  1.0  \n",
       "4185                       0.0                  1.0  \n",
       "28664                      0.0                  1.0  \n",
       "11874                      0.0                  1.0  \n",
       "11431                      0.0                  1.0  \n",
       "\n",
       "[40749 rows x 23 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transform = preprocessing.StandardScaler()\n",
    "X_new[['luxury_sales','fresh_sales','dry_sales']] = transform.fit_transform(X_new[['luxury_sales','fresh_sales','dry_sales']])\n",
    "X_new"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c60bb2e4",
   "metadata": {
    "papermill": {
     "duration": 0.022589,
     "end_time": "2024-05-17T05:49:35.132580",
     "exception": false,
     "start_time": "2024-05-17T05:49:35.109991",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Use one vs all to predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "89964a0f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:49:35.185027Z",
     "iopub.status.busy": "2024-05-17T05:49:35.184619Z",
     "iopub.status.idle": "2024-05-17T05:49:35.244244Z",
     "shell.execute_reply": "2024-05-17T05:49:35.243242Z"
    },
    "papermill": {
     "duration": 0.090348,
     "end_time": "2024-05-17T05:49:35.248468",
     "exception": false,
     "start_time": "2024-05-17T05:49:35.158120",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "Y_ovr_new = ovr.predict(X_new)\n",
    "test_data['cluster_category'] = Y_ovr_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d84e38c8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:49:35.325883Z",
     "iopub.status.busy": "2024-05-17T05:49:35.325282Z",
     "iopub.status.idle": "2024-05-17T05:49:35.340194Z",
     "shell.execute_reply": "2024-05-17T05:49:35.338897Z"
    },
    "papermill": {
     "duration": 0.056955,
     "end_time": "2024-05-17T05:49:35.343405",
     "exception": false,
     "start_time": "2024-05-17T05:49:35.286450",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster_category\n",
       "1.0    9956\n",
       "4.0    9067\n",
       "6.0    8891\n",
       "2.0    8177\n",
       "3.0    2576\n",
       "5.0    2082\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data['cluster_category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "46839ba0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:49:35.397977Z",
     "iopub.status.busy": "2024-05-17T05:49:35.397542Z",
     "iopub.status.idle": "2024-05-17T05:49:35.419834Z",
     "shell.execute_reply": "2024-05-17T05:49:35.418697Z"
    },
    "papermill": {
     "duration": 0.049041,
     "end_time": "2024-05-17T05:49:35.422111",
     "exception": false,
     "start_time": "2024-05-17T05:49:35.373070",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster_catgeory\n",
       "1.0    188297\n",
       "4.0    171485\n",
       "6.0    168624\n",
       "2.0    154355\n",
       "3.0     48842\n",
       "5.0     39492\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['cluster_catgeory'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9773a14e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:49:35.470201Z",
     "iopub.status.busy": "2024-05-17T05:49:35.469431Z",
     "iopub.status.idle": "2024-05-17T05:49:35.485374Z",
     "shell.execute_reply": "2024-05-17T05:49:35.484512Z"
    },
    "papermill": {
     "duration": 0.042734,
     "end_time": "2024-05-17T05:49:35.487647",
     "exception": false,
     "start_time": "2024-05-17T05:49:35.444913",
     "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>Customer_ID</th>\n",
       "      <th>outlet_city</th>\n",
       "      <th>luxury_sales</th>\n",
       "      <th>fresh_sales</th>\n",
       "      <th>dry_sales</th>\n",
       "      <th>cluster_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33574</td>\n",
       "      <td>Batticaloa</td>\n",
       "      <td>2686.50</td>\n",
       "      <td>3582.00</td>\n",
       "      <td>12537.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10089</td>\n",
       "      <td>Batticaloa</td>\n",
       "      <td>1717.56</td>\n",
       "      <td>2576.34</td>\n",
       "      <td>9446.58</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38329</td>\n",
       "      <td>Batticaloa</td>\n",
       "      <td>854.04</td>\n",
       "      <td>1242.24</td>\n",
       "      <td>5201.88</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11376</td>\n",
       "      <td>Batticaloa</td>\n",
       "      <td>1638.12</td>\n",
       "      <td>2320.67</td>\n",
       "      <td>9282.68</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12410</td>\n",
       "      <td>Batticaloa</td>\n",
       "      <td>1039.09</td>\n",
       "      <td>1518.67</td>\n",
       "      <td>5435.24</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer_ID outlet_city  luxury_sales  fresh_sales  dry_sales  \\\n",
       "0        33574  Batticaloa       2686.50      3582.00   12537.00   \n",
       "1        10089  Batticaloa       1717.56      2576.34    9446.58   \n",
       "2        38329  Batticaloa        854.04      1242.24    5201.88   \n",
       "3        11376  Batticaloa       1638.12      2320.67    9282.68   \n",
       "4        12410  Batticaloa       1039.09      1518.67    5435.24   \n",
       "\n",
       "   cluster_category  \n",
       "0               1.0  \n",
       "1               1.0  \n",
       "2               1.0  \n",
       "3               1.0  \n",
       "4               1.0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data = test_data.reset_index().rename(columns={'index': 'Customer_ID'})\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa6f0277",
   "metadata": {
    "papermill": {
     "duration": 0.021407,
     "end_time": "2024-05-17T05:49:35.530410",
     "exception": false,
     "start_time": "2024-05-17T05:49:35.509003",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Save data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "759449de",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T05:49:35.574706Z",
     "iopub.status.busy": "2024-05-17T05:49:35.574094Z",
     "iopub.status.idle": "2024-05-17T05:49:35.629869Z",
     "shell.execute_reply": "2024-05-17T05:49:35.628627Z"
    },
    "papermill": {
     "duration": 0.081255,
     "end_time": "2024-05-17T05:49:35.632882",
     "exception": false,
     "start_time": "2024-05-17T05:49:35.551627",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_data[['Customer_ID','cluster_category']].to_csv('final_result.csv',index=False)"
   ]
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "none",
   "dataSources": [
    {
     "databundleVersionId": 8564874,
     "sourceId": 78060,
     "sourceType": "competition"
    }
   ],
   "dockerImageVersionId": 30698,
   "isGpuEnabled": false,
   "isInternetEnabled": false,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 145.448886,
   "end_time": "2024-05-17T05:49:36.377422",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2024-05-17T05:47:10.928536",
   "version": "2.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}