{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "import pandas as pd\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('model.pkl', 'rb') as model_pipeline:\n",
    "    model = pickle.load(model_pipeline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>warehouse_block</th>\n",
       "      <th>mode_of_shipment</th>\n",
       "      <th>customer_care_calls</th>\n",
       "      <th>customer_rating</th>\n",
       "      <th>cost_of_the_product</th>\n",
       "      <th>prior_purchases</th>\n",
       "      <th>product_importance</th>\n",
       "      <th>gender</th>\n",
       "      <th>discount_offered</th>\n",
       "      <th>weight_in_gms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>Flight</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>300</td>\n",
       "      <td>6</td>\n",
       "      <td>medium</td>\n",
       "      <td>M</td>\n",
       "      <td>30</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>D</td>\n",
       "      <td>Road</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>low</td>\n",
       "      <td>F</td>\n",
       "      <td>10</td>\n",
       "      <td>3800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  warehouse_block mode_of_shipment  customer_care_calls  customer_rating  \\\n",
       "0               A           Flight                    1                5   \n",
       "1               D             Road                    5                1   \n",
       "\n",
       "   cost_of_the_product  prior_purchases product_importance gender  \\\n",
       "0                  300                6             medium      M   \n",
       "1                   20                2                low      F   \n",
       "\n",
       "   discount_offered  weight_in_gms  \n",
       "0                30           3500  \n",
       "1                10           3800  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_inf = {\n",
    "    'warehouse_block':['A','D'] , # Block Warehouse A, Block Warehouse D\n",
    "    'mode_of_shipment': ['Flight','Road'], # Shipment Flight, Road\n",
    "    'customer_care_calls':[1,5], # Enquiry Calls 1 Time, Enquiry Calls 5 Times\n",
    "    'customer_rating': [5,1],  # Rating 5 (highest), Rating 1 (Lowest)\n",
    "    'cost_of_the_product':[300,20], # Cost $300, Cost $20\n",
    "    'prior_purchases':[6,2], # Prior Purchase 6 Times Before, Prior Purchase 2 Times Before\n",
    "    'product_importance':['medium','low'], # Importance Product Medium, Importance Product Low\n",
    "    'gender':['M','F'], # Male, Female\n",
    "    'discount_offered':[30,10], # Discount 30%, Discount 10%\n",
    "    'weight_in_gms':[3500,3800], # Weight 3500 grams, Weight 3800 grams\n",
    "}\n",
    "data_inf = pd.DataFrame(data_inf)\n",
    "data_inf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def label_cluster(cluster_number):\n",
    "    if cluster_number == 0:\n",
    "        return \"Shipping Not On Time !\"\n",
    "    elif cluster_number == 1:\n",
    "        return \"Shipping On Time !\"\n",
    "    else:\n",
    "        return \"Unknown\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Shipping On Time !', 'Shipping Not On Time !']\n"
     ]
    }
   ],
   "source": [
    "data_inf_pred = model.predict(data_inf)\n",
    "data_inf_pred\n",
    "\n",
    "labels = [label_cluster(cluster) for cluster in data_inf_pred]\n",
    "print(labels)"
   ]
  }
 ],
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