{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Machine Learning 2695 Coding Exam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This part of the exam will carry 50% of the exam grade, and the theoretical part will carry the other 50%. You have in total 2 hours to complete both parts. For the theoretical part, you will complete the moodle quiz, do not forget to submit it once you are done (multiple submissions are possible, the last one counts).\n",
    "\n",
    "You should submit this notebook through Coding Exam submission on Moodle. \n",
    "\n",
    "The notebook should contain the code, output and explanations. The notebook should be rerunable.\n",
    "\n",
    "Total points: **30**\n",
    "\n",
    "- Question 1: 10 points\n",
    "- Question 2: 13 points\n",
    "- Question 3: 7 points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 1\n",
    "\n",
    "**10 points**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You are hired by the Inventory department of a large retail store to help with the product inventory management. They provided you with a product dataset stored in a file: `Question_1.csv` with the following information:\n",
    "\n",
    "- `product_id`:  Unique identifier for each product in the inventory.\n",
    "- `sales_volume`: Total quantity of items sold over a specific period.\n",
    "- `location`: Warehouse or store location where the product is stored.\n",
    "- `category`: Type of products (e.g., electronics, clothing, groceries).\n",
    "- `demand_level` : Level of product demand.\n",
    "- `inventory_count` :  Current inventory count for each item.\n",
    "- `reorder_frequency`: Number of times an item is reordered in the last 3 months.\n",
    "- `lead_time`: Average time taken from ordering to receiving the item, in days.\n",
    "- `stock_priority`: Priority level for restocking.\n",
    "- `supplier`: Name or code of the supplier providing the product.\n",
    "\n",
    "1. Your task is to use the most appropriate preprocessing steps and suggest the most appropriate number of product groups. Justify all your decisions.\n",
    "2. Assuming that a data point (product) is considered a core product if it has 10 neighbors (including itself), and the maximum distance between two data points in the neighborhood is 1.5, how many products would not belong to a single group? How many groups would we end up having? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>product_id</th>\n",
       "      <th>sales_volume</th>\n",
       "      <th>location</th>\n",
       "      <th>category</th>\n",
       "      <th>demand_level</th>\n",
       "      <th>inventory_count</th>\n",
       "      <th>reorder_frequency</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>stock_priority</th>\n",
       "      <th>supplier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>Rivertown</td>\n",
       "      <td>shoes</td>\n",
       "      <td>medium</td>\n",
       "      <td>5736.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>not_critical</td>\n",
       "      <td>Nimbus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>371.0</td>\n",
       "      <td>Rivertown</td>\n",
       "      <td>shoes</td>\n",
       "      <td>low</td>\n",
       "      <td>4951.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>not_critical</td>\n",
       "      <td>Nimbus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>482.0</td>\n",
       "      <td>Rivertown</td>\n",
       "      <td>shoes</td>\n",
       "      <td>low</td>\n",
       "      <td>4651.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>not_critical</td>\n",
       "      <td>Vertex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>494.0</td>\n",
       "      <td>Rivertown</td>\n",
       "      <td>electronics</td>\n",
       "      <td>medium</td>\n",
       "      <td>3406.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>lowest</td>\n",
       "      <td>Pinnacle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>298.0</td>\n",
       "      <td>Rivertown</td>\n",
       "      <td>accessories</td>\n",
       "      <td>medium</td>\n",
       "      <td>4257.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>not_critical</td>\n",
       "      <td>Vertex</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_id  sales_volume   location     category demand_level  \\\n",
       "0           0         255.0  Rivertown        shoes       medium   \n",
       "1           1         371.0  Rivertown        shoes          low   \n",
       "2           2         482.0  Rivertown        shoes          low   \n",
       "3           3         494.0  Rivertown  electronics       medium   \n",
       "4           4         298.0  Rivertown  accessories       medium   \n",
       "\n",
       "   inventory_count  reorder_frequency  lead_time stock_priority  supplier  \n",
       "0           5736.0                2.0       28.0   not_critical    Nimbus  \n",
       "1           4951.0                5.0       86.0   not_critical    Nimbus  \n",
       "2           4651.0                4.0       22.0   not_critical    Vertex  \n",
       "3           3406.0                3.0      163.0         lowest  Pinnacle  \n",
       "4           4257.0                3.0       14.0   not_critical    Vertex  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('Question_1.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product_id            0\n",
       "sales_volume         30\n",
       "location             36\n",
       "category              0\n",
       "demand_level          0\n",
       "inventory_count       0\n",
       "reorder_frequency     0\n",
       "lead_time             0\n",
       "stock_priority        0\n",
       "supplier              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no missing values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['product_id', 'sales_volume', 'location', 'category', 'demand_level',\n",
       "       'inventory_count', 'reorder_frequency', 'lead_time', 'stock_priority',\n",
       "       'supplier'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product_id             int64\n",
       "sales_volume         float64\n",
       "location              object\n",
       "category              object\n",
       "demand_level          object\n",
       "inventory_count      float64\n",
       "reorder_frequency    float64\n",
       "lead_time            float64\n",
       "stock_priority        object\n",
       "supplier              object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are both numerical and categorical data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(columns='product_id') #non-informative variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sales_volume', 'inventory_count', 'reorder_frequency', 'lead_time'], dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical=df.select_dtypes(include=np.number).columns\n",
    "numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['location', 'category', 'demand_level', 'stock_priority', 'supplier'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical=df.select_dtypes(exclude=np.number).columns\n",
    "categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "location\n",
      "Rivertown    2742\n",
      "Everton       119\n",
      "Westvale      103\n",
      "Name: count, dtype: int64\n",
      "\n",
      "category\n",
      "shoes          1500\n",
      "accessories     900\n",
      "groceries       240\n",
      "electronics     150\n",
      "cosmetics       150\n",
      "clothing         60\n",
      "Name: count, dtype: int64\n",
      "\n",
      "demand_level\n",
      "low       1650\n",
      "medium     900\n",
      "high       450\n",
      "Name: count, dtype: int64\n",
      "\n",
      "stock_priority\n",
      "not_critical     1830\n",
      "low_critical      600\n",
      "lowest            420\n",
      "high_critical     150\n",
      "Name: count, dtype: int64\n",
      "\n",
      "supplier\n",
      "Nimbus      1800\n",
      "Pinnacle     600\n",
      "Vertex       450\n",
      "Zenith        90\n",
      "Acme          60\n",
      "Name: count, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for col in categorical:\n",
    "    print(df[col].value_counts())\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Among the categorical variables, `demand_level` and `stock_priority` are ordinal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ohe_cols=['location', 'category', 'supplier']\n",
    "ord_cols=['stock_priority', 'demand_level']\n",
    "categories=[[\"lowest\", \"not_critical\", \"low_critical\", 'high_critical'],\n",
    "            ['low','medium','high']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler, OneHotEncoder, OrdinalEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;encoder&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;numerical&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputation_median&#x27;,\n",
       "                                                                   SimpleImputer())]),\n",
       "                                                  Index([&#x27;sales_volume&#x27;, &#x27;inventory_count&#x27;, &#x27;reorder_frequency&#x27;, &#x27;lead_time&#x27;], dtype=&#x27;object&#x27;)),\n",
       "                                                 (&#x27;ohe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputation_mode&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;onehot&#x27;,\n",
       "                                                                   OneHotEncoder(sparse_output=False))]),\n",
       "                                                  [&#x27;location&#x27;, &#x27;category&#x27;,\n",
       "                                                   &#x27;supplier&#x27;]),\n",
       "                                                 (&#x27;ordinal&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;ord&#x27;,\n",
       "                                                                   OrdinalEncoder(categories=[[&#x27;lowest&#x27;,\n",
       "                                                                                               &#x27;not_critical&#x27;,\n",
       "                                                                                               &#x27;low_critical&#x27;,\n",
       "                                                                                               &#x27;high_critical&#x27;],\n",
       "                                                                                              [&#x27;low&#x27;,\n",
       "                                                                                               &#x27;medium&#x27;,\n",
       "                                                                                               &#x27;high&#x27;]]))]),\n",
       "                                                  [&#x27;stock_priority&#x27;,\n",
       "                                                   &#x27;demand_level&#x27;])])),\n",
       "                (&#x27;scaler&#x27;, StandardScaler())])</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 \">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;encoder&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;numerical&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputation_median&#x27;,\n",
       "                                                                   SimpleImputer())]),\n",
       "                                                  Index([&#x27;sales_volume&#x27;, &#x27;inventory_count&#x27;, &#x27;reorder_frequency&#x27;, &#x27;lead_time&#x27;], dtype=&#x27;object&#x27;)),\n",
       "                                                 (&#x27;ohe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputation_mode&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;onehot&#x27;,\n",
       "                                                                   OneHotEncoder(sparse_output=False))]),\n",
       "                                                  [&#x27;location&#x27;, &#x27;category&#x27;,\n",
       "                                                   &#x27;supplier&#x27;]),\n",
       "                                                 (&#x27;ordinal&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;ord&#x27;,\n",
       "                                                                   OrdinalEncoder(categories=[[&#x27;lowest&#x27;,\n",
       "                                                                                               &#x27;not_critical&#x27;,\n",
       "                                                                                               &#x27;low_critical&#x27;,\n",
       "                                                                                               &#x27;high_critical&#x27;],\n",
       "                                                                                              [&#x27;low&#x27;,\n",
       "                                                                                               &#x27;medium&#x27;,\n",
       "                                                                                               &#x27;high&#x27;]]))]),\n",
       "                                                  [&#x27;stock_priority&#x27;,\n",
       "                                                   &#x27;demand_level&#x27;])])),\n",
       "                (&#x27;scaler&#x27;, StandardScaler())])</pre></div> </div></div><div class=\"sk-serial\"><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-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;encoder: ColumnTransformer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for encoder: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;numerical&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputation_median&#x27;,\n",
       "                                                  SimpleImputer())]),\n",
       "                                 Index([&#x27;sales_volume&#x27;, &#x27;inventory_count&#x27;, &#x27;reorder_frequency&#x27;, &#x27;lead_time&#x27;], dtype=&#x27;object&#x27;)),\n",
       "                                (&#x27;ohe&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputation_mode&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;onehot&#x27;,\n",
       "                                                  OneHotEncoder(sparse_output=False))]),\n",
       "                                 [&#x27;location&#x27;, &#x27;category&#x27;, &#x27;supplier&#x27;]),\n",
       "                                (&#x27;ordinal&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;ord&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;lowest&#x27;,\n",
       "                                                                              &#x27;not_critical&#x27;,\n",
       "                                                                              &#x27;low_critical&#x27;,\n",
       "                                                                              &#x27;high_critical&#x27;],\n",
       "                                                                             [&#x27;low&#x27;,\n",
       "                                                                              &#x27;medium&#x27;,\n",
       "                                                                              &#x27;high&#x27;]]))]),\n",
       "                                 [&#x27;stock_priority&#x27;, &#x27;demand_level&#x27;])])</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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">numerical</label><div class=\"sk-toggleable__content \"><pre>Index([&#x27;sales_volume&#x27;, &#x27;inventory_count&#x27;, &#x27;reorder_frequency&#x27;, &#x27;lead_time&#x27;], dtype=&#x27;object&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content \"><pre>SimpleImputer()</pre></div> </div></div></div></div></div></div></div><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 \">ohe</label><div class=\"sk-toggleable__content \"><pre>[&#x27;location&#x27;, &#x27;category&#x27;, &#x27;supplier&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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 \">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content \"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content \"><pre>OneHotEncoder(sparse_output=False)</pre></div> </div></div></div></div></div></div></div><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-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">ordinal</label><div class=\"sk-toggleable__content \"><pre>[&#x27;stock_priority&#x27;, &#x27;demand_level&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;OrdinalEncoder<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></label><div class=\"sk-toggleable__content \"><pre>OrdinalEncoder(categories=[[&#x27;lowest&#x27;, &#x27;not_critical&#x27;, &#x27;low_critical&#x27;,\n",
       "                            &#x27;high_critical&#x27;],\n",
       "                           [&#x27;low&#x27;, &#x27;medium&#x27;, &#x27;high&#x27;]])</pre></div> </div></div></div></div></div></div></div><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-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">remainder</label><div class=\"sk-toggleable__content \"><pre></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-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">passthrough</label><div class=\"sk-toggleable__content \"><pre>passthrough</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content \"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('encoder',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('numerical',\n",
       "                                                  Pipeline(steps=[('imputation_median',\n",
       "                                                                   SimpleImputer())]),\n",
       "                                                  Index(['sales_volume', 'inventory_count', 'reorder_frequency', 'lead_time'], dtype='object')),\n",
       "                                                 ('ohe',\n",
       "                                                  Pipeline(steps=[('imputation_mode',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('onehot',\n",
       "                                                                   OneHotEncoder(sparse_output=False))]),\n",
       "                                                  ['location', 'category',\n",
       "                                                   'supplier']),\n",
       "                                                 ('ordinal',\n",
       "                                                  Pipeline(steps=[('ord',\n",
       "                                                                   OrdinalEncoder(categories=[['lowest',\n",
       "                                                                                               'not_critical',\n",
       "                                                                                               'low_critical',\n",
       "                                                                                               'high_critical'],\n",
       "                                                                                              ['low',\n",
       "                                                                                               'medium',\n",
       "                                                                                               'high']]))]),\n",
       "                                                  ['stock_priority',\n",
       "                                                   'demand_level'])])),\n",
       "                ('scaler', StandardScaler())])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_preprocessor = Pipeline([ \n",
    "    (\"imputation_median\", SimpleImputer(strategy=\"mean\"))\n",
    "])\n",
    "\n",
    "ohe_preprocessor = Pipeline([\n",
    "        (\"imputation_mode\", SimpleImputer( strategy=\"most_frequent\")),\n",
    "        (\"onehot\", OneHotEncoder(sparse_output=False ))\n",
    "    ])\n",
    "\n",
    "ord_preprocessor = Pipeline([\n",
    "\n",
    "        (\"ord\", OrdinalEncoder(categories=categories ))\n",
    "    ])\n",
    "\n",
    "\n",
    "preprocessor = ColumnTransformer([\n",
    "        (\"numerical\", numeric_preprocessor, numerical),\n",
    "        (\"ohe\", ohe_preprocessor, ohe_cols),\n",
    "        (\"ordinal\", ord_preprocessor, ord_cols)\n",
    "    ], remainder='passthrough') #this step is necessary, otherwise the numerical variables are discarded.\n",
    "\n",
    "pipe = Pipeline([\n",
    "     ('encoder', preprocessor),\n",
    "     ('scaler', StandardScaler()),  \n",
    "   ])\n",
    "pipe\n",
    "                                 \n",
    "                                 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_transformed=pipe.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>sales_volume</th>\n",
       "      <th>location</th>\n",
       "      <th>category</th>\n",
       "      <th>demand_level</th>\n",
       "      <th>inventory_count</th>\n",
       "      <th>reorder_frequency</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>stock_priority</th>\n",
       "      <th>supplier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>255.0</td>\n",
       "      <td>Rivertown</td>\n",
       "      <td>shoes</td>\n",
       "      <td>medium</td>\n",
       "      <td>5736.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>not_critical</td>\n",
       "      <td>Nimbus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>371.0</td>\n",
       "      <td>Rivertown</td>\n",
       "      <td>shoes</td>\n",
       "      <td>low</td>\n",
       "      <td>4951.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>not_critical</td>\n",
       "      <td>Nimbus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>482.0</td>\n",
       "      <td>Rivertown</td>\n",
       "      <td>shoes</td>\n",
       "      <td>low</td>\n",
       "      <td>4651.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>not_critical</td>\n",
       "      <td>Vertex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>494.0</td>\n",
       "      <td>Rivertown</td>\n",
       "      <td>electronics</td>\n",
       "      <td>medium</td>\n",
       "      <td>3406.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>lowest</td>\n",
       "      <td>Pinnacle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>298.0</td>\n",
       "      <td>Rivertown</td>\n",
       "      <td>accessories</td>\n",
       "      <td>medium</td>\n",
       "      <td>4257.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>not_critical</td>\n",
       "      <td>Vertex</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sales_volume   location     category demand_level  inventory_count  \\\n",
       "0         255.0  Rivertown        shoes       medium           5736.0   \n",
       "1         371.0  Rivertown        shoes          low           4951.0   \n",
       "2         482.0  Rivertown        shoes          low           4651.0   \n",
       "3         494.0  Rivertown  electronics       medium           3406.0   \n",
       "4         298.0  Rivertown  accessories       medium           4257.0   \n",
       "\n",
       "   reorder_frequency  lead_time stock_priority  supplier  \n",
       "0                2.0       28.0   not_critical    Nimbus  \n",
       "1                5.0       86.0   not_critical    Nimbus  \n",
       "2                4.0       22.0   not_critical    Vertex  \n",
       "3                3.0      163.0         lowest  Pinnacle  \n",
       "4                3.0       14.0   not_critical    Vertex  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['numerical__sales_volume', 'numerical__inventory_count',\n",
       "       'numerical__reorder_frequency', 'numerical__lead_time',\n",
       "       'ohe__location_Everton', 'ohe__location_Rivertown',\n",
       "       'ohe__location_Westvale', 'ohe__category_accessories',\n",
       "       'ohe__category_clothing', 'ohe__category_cosmetics',\n",
       "       'ohe__category_electronics', 'ohe__category_groceries',\n",
       "       'ohe__category_shoes', 'ohe__supplier_Acme',\n",
       "       'ohe__supplier_Nimbus', 'ohe__supplier_Pinnacle',\n",
       "       'ohe__supplier_Vertex', 'ohe__supplier_Zenith',\n",
       "       'ordinal__stock_priority', 'ordinal__demand_level'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_names=pipe.named_steps['encoder'].get_feature_names_out()\n",
    "feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3000, 20)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transformed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score,  davies_bouldin_score\n",
    "scores = {'SSE': [], 'Silhouette Coefficient': [], 'David Bouldin Score': []}\n",
    "\n",
    "for k in range(2,16):\n",
    "    \n",
    "    kmeans = KMeans(n_clusters = k, init = 'k-means++', random_state=42)\n",
    "    kmeans.fit(df_transformed)\n",
    "    silhouette_results = silhouette_score(df_transformed, kmeans.labels_)\n",
    "    DB_results = davies_bouldin_score(df_transformed, kmeans.labels_)\n",
    "    \n",
    "    scores['SSE'].append(kmeans.inertia_)\n",
    "    scores['Silhouette Coefficient'].append(silhouette_results)\n",
    "    scores['David Bouldin Score'].append(DB_results)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2500x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows = 1, ncols = 3, figsize=(25,6))\n",
    "\n",
    "for score_i, ax in zip(scores, axes.ravel()):\n",
    "    ax.plot(range(2,16), scores[score_i])\n",
    "    ax.set_xlabel('Number of Clusters', fontsize=12)\n",
    "    ax.set_ylabel(score_i, fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SSE: The most prominent elbows astarts at 4 clusters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Silhouette: Local maximum present at 4, 5, 9 clusters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "David Bouldin: Local minimum at 5 clusters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Therefore, the appropriate number of clusters is 5. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_cluster=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=5, random_state=42)</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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KMeans<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KMeans(n_clusters=5, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=5, random_state=42)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=no_cluster, init = 'k-means++', random_state=42)\n",
    "kmeans.fit(df_transformed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The count of instances per cluster is as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({1: 1709, 2: 601, 4: 450, 3: 150, 0: 90})"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "Counter(kmeans.labels_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using TSNE we can get a visual sense of the clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "tsne = TSNE(random_state=42,  perplexity = 30)\n",
    "X_tsne = tsne.fit_transform(df_transformed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 1121,\n",
       "         7: 333,\n",
       "         4: 237,\n",
       "         1: 213,\n",
       "         2: 148,\n",
       "         5: 147,\n",
       "         8: 146,\n",
       "         3: 145,\n",
       "         13: 99,\n",
       "         11: 84,\n",
       "         9: 82,\n",
       "         12: 60,\n",
       "         6: 58,\n",
       "         10: 58,\n",
       "         -1: 57,\n",
       "         14: 12})"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "dbscan = DBSCAN(eps=1.5, min_samples=10)\n",
    "dbscan.fit(df_transformed)\n",
    "Counter(dbscan.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 't-SNE feature 1')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6))\n",
    "plt.scatter(X_tsne[:, 0], X_tsne[:, 1],  s=30, c=dbscan.labels_ , cmap=plt.cm.Spectral)\n",
    "plt.xlabel(\"t-SNE feature 0\")\n",
    "plt.ylabel(\"t-SNE feature 1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_labels=[ 'red' if label==-1 else 'blue' for label in dbscan.labels_  ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 't-SNE feature 1')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6))\n",
    "plt.scatter(X_tsne[:, 0], X_tsne[:, 1],  s=30, c=new_labels)\n",
    "plt.xlabel(\"t-SNE feature 0\")\n",
    "plt.ylabel(\"t-SNE feature 1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2\n",
    "\n",
    "**13 points**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You are hired by the Sales department of a large retail store to predict future sales volume based on past sales data. They provided you with a dataset stored in a file: `Question_2.csv` with the following information:\n",
    "- `past_sales`: Historical sales volume data for previous periods.\n",
    "- `price`: The price of the product during the prediction period.\n",
    "- `season`: The seasons to capture seasonal trends.\n",
    "- `product_category`: The category of the product.\n",
    "- `store_location_type`: The type of the location of the store.\n",
    "- `advertising_spend`: Amount of money spent on advertising for the product.\n",
    "- `discount_rate`: The percentage discount applied to the product.\n",
    "- `competitor_sales_volume`: Sales volumes of similar products sold by competitors.\n",
    "- `promotion_type`: The type of promotion applied.\n",
    "- `store_traffic_volume`: The foot traffic levels in the store.\n",
    "- `sales_volume`: The volume of sales that we wish to predict in the future.\n",
    "\n",
    "The goal is to develop two approaches:   \n",
    "- A. One that uses an ensemble method that predicts the exact volume of sales.  \n",
    "- B. One that uses an ensemble method to predict the level of sales volume, where the level of sales volume should be: \n",
    "    - 0: if below 4500 (low level), \n",
    "    - 1: if equal or larger than 4500 and less than 6500 (medium level)\n",
    "    - 2: if equal or larger than 6500 (high level)\n",
    "\n",
    "At least one parameter of each model should be tuned, and the metric of optimization should be justified.\n",
    "State the final performance of both models.\n",
    "\n",
    "Regarding model B, additionally, the sales team would like to know what percentage of all sales of high level the model wrongly identifies as medium level, out of all the high level ones. Show how you have arrived at the answer and/or display a visual that supports your answer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To begin with, the data should be inspected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "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>past_sales</th>\n",
       "      <th>price</th>\n",
       "      <th>season</th>\n",
       "      <th>product_category</th>\n",
       "      <th>store_location_type</th>\n",
       "      <th>advertising_spend</th>\n",
       "      <th>discount_rate</th>\n",
       "      <th>competitor_sales_volume</th>\n",
       "      <th>promotion_type</th>\n",
       "      <th>store_traffic_volume</th>\n",
       "      <th>sales_volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3712.019206</td>\n",
       "      <td>61.946333</td>\n",
       "      <td>Summer</td>\n",
       "      <td>shoes</td>\n",
       "      <td>suburban</td>\n",
       "      <td>682.836888</td>\n",
       "      <td>28.070782</td>\n",
       "      <td>4198.959000</td>\n",
       "      <td>percentage_off</td>\n",
       "      <td>Low</td>\n",
       "      <td>4964.299973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4666.499402</td>\n",
       "      <td>47.336866</td>\n",
       "      <td>Spring</td>\n",
       "      <td>electronics</td>\n",
       "      <td>downtown</td>\n",
       "      <td>572.218433</td>\n",
       "      <td>17.405044</td>\n",
       "      <td>6576.442539</td>\n",
       "      <td>percentage_off</td>\n",
       "      <td>Medium</td>\n",
       "      <td>4006.739512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6085.299988</td>\n",
       "      <td>54.793826</td>\n",
       "      <td>Spring</td>\n",
       "      <td>cosmetics</td>\n",
       "      <td>downtown</td>\n",
       "      <td>466.107517</td>\n",
       "      <td>17.719031</td>\n",
       "      <td>4641.982873</td>\n",
       "      <td>percentage_off</td>\n",
       "      <td>Low</td>\n",
       "      <td>4295.756331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6102.391928</td>\n",
       "      <td>94.596118</td>\n",
       "      <td>Spring</td>\n",
       "      <td>clothing</td>\n",
       "      <td>downtown</td>\n",
       "      <td>388.514092</td>\n",
       "      <td>22.095615</td>\n",
       "      <td>9055.830815</td>\n",
       "      <td>loyalty_points</td>\n",
       "      <td>Low</td>\n",
       "      <td>6978.062512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3014.237193</td>\n",
       "      <td>65.518271</td>\n",
       "      <td>Summer</td>\n",
       "      <td>cosmetics</td>\n",
       "      <td>suburban</td>\n",
       "      <td>559.756395</td>\n",
       "      <td>15.494180</td>\n",
       "      <td>5762.883494</td>\n",
       "      <td>buy_one_get_one</td>\n",
       "      <td>Low</td>\n",
       "      <td>3499.905963</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    past_sales      price  season product_category store_location_type  \\\n",
       "0  3712.019206  61.946333  Summer            shoes            suburban   \n",
       "1  4666.499402  47.336866  Spring      electronics            downtown   \n",
       "2  6085.299988  54.793826  Spring        cosmetics            downtown   \n",
       "3  6102.391928  94.596118  Spring         clothing            downtown   \n",
       "4  3014.237193  65.518271  Summer        cosmetics            suburban   \n",
       "\n",
       "   advertising_spend  discount_rate  competitor_sales_volume   promotion_type  \\\n",
       "0         682.836888      28.070782              4198.959000   percentage_off   \n",
       "1         572.218433      17.405044              6576.442539   percentage_off   \n",
       "2         466.107517      17.719031              4641.982873   percentage_off   \n",
       "3         388.514092      22.095615              9055.830815   loyalty_points   \n",
       "4         559.756395      15.494180              5762.883494  buy_one_get_one   \n",
       "\n",
       "  store_traffic_volume  sales_volume  \n",
       "0                  Low   4964.299973  \n",
       "1               Medium   4006.739512  \n",
       "2                  Low   4295.756331  \n",
       "3                  Low   6978.062512  \n",
       "4                  Low   3499.905963  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('Question_2.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3000, 11)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "past_sales                 float64\n",
       "price                      float64\n",
       "season                      object\n",
       "product_category            object\n",
       "store_location_type         object\n",
       "advertising_spend          float64\n",
       "discount_rate              float64\n",
       "competitor_sales_volume    float64\n",
       "promotion_type              object\n",
       "store_traffic_volume        object\n",
       "sales_volume               float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given the data types and the values of each variable in the first 5 rows, it may be inferred that all variables are numerical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "past_sales                 15\n",
       "price                      28\n",
       "season                      7\n",
       "product_category            4\n",
       "store_location_type         0\n",
       "advertising_spend           0\n",
       "discount_rate              11\n",
       "competitor_sales_volume     0\n",
       "promotion_type              0\n",
       "store_traffic_volume       16\n",
       "sales_volume                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Missing values should be addressed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>past_sales</th>\n",
       "      <th>price</th>\n",
       "      <th>season</th>\n",
       "      <th>product_category</th>\n",
       "      <th>store_location_type</th>\n",
       "      <th>advertising_spend</th>\n",
       "      <th>discount_rate</th>\n",
       "      <th>competitor_sales_volume</th>\n",
       "      <th>promotion_type</th>\n",
       "      <th>store_traffic_volume</th>\n",
       "      <th>sales_volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [past_sales, price, season, product_category, store_location_type, advertising_spend, discount_rate, competitor_sales_volume, promotion_type, store_traffic_volume, sales_volume]\n",
       "Index: []"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2400, 10)\n",
      "(600, 10)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(df.drop(columns='sales_volume'), df['sales_volume'], random_state=0, test_size=0.2)\n",
    "\n",
    "print(X_train.shape)\n",
    "\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['past_sales', 'price', 'advertising_spend', 'discount_rate',\n",
       "       'competitor_sales_volume'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical=X_train.select_dtypes(include=np.number).columns\n",
    "numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['season', 'product_category', 'store_location_type', 'promotion_type',\n",
       "       'store_traffic_volume'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical=X_train.select_dtypes(exclude=np.number).columns\n",
    "categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "season\n",
      "Autumn    896\n",
      "Summer    750\n",
      "Spring    747\n",
      "Winter    600\n",
      "Name: count, dtype: int64\n",
      "\n",
      "product_category\n",
      "groceries      750\n",
      "clothing       747\n",
      "electronics    450\n",
      "accessories    450\n",
      "shoes          300\n",
      "cosmetics      299\n",
      "Name: count, dtype: int64\n",
      "\n",
      "store_location_type\n",
      "suburban    1350\n",
      "downtown    1050\n",
      "rural        600\n",
      "Name: count, dtype: int64\n",
      "\n",
      "promotion_type\n",
      "percentage_off     1350\n",
      "loyalty_points      900\n",
      "buy_one_get_one     750\n",
      "Name: count, dtype: int64\n",
      "\n",
      "store_traffic_volume\n",
      "Low         1791\n",
      "High         449\n",
      "Medium       446\n",
      "VeryLow      150\n",
      "VeryHigh     148\n",
      "Name: count, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for col in categorical:\n",
    "    print(df[col].value_counts())\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Among the categorical variables, `season` and `store_traffic_volume` are ordinal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "ohe_cols=['product_category', 'store_location_type', 'promotion_type']\n",
    "ord_cols=['season', 'store_traffic_volume']\n",
    "categories=[['Spring', 'Summer', 'Autumn', 'Winter'],\n",
    "            ['VeryLow', 'Low', 'Medium', 'High', 'VeryHigh']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV  \n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;encoder&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;numerical&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputation_median&#x27;,\n",
       "                                                                   SimpleImputer())]),\n",
       "                                                  Index([&#x27;past_sales&#x27;, &#x27;price&#x27;, &#x27;advertising_spend&#x27;, &#x27;discount_rate&#x27;,\n",
       "       &#x27;competitor_sales_volume&#x27;],\n",
       "      dtype=&#x27;object&#x27;)),\n",
       "                                                 (&#x27;ohe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputation_mode&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;onehot&#x27;,\n",
       "                                                                   OneHotEncoder(sparse_output=False))]),\n",
       "                                                  [&#x27;product_category&#x27;,\n",
       "                                                   &#x27;store_location_type&#x27;,\n",
       "                                                   &#x27;promotion_type&#x27;]),\n",
       "                                                 (&#x27;ordinal&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputation_mode&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;ord&#x27;,\n",
       "                                                                   OrdinalEncoder(categories=[[&#x27;Spring&#x27;,\n",
       "                                                                                               &#x27;Summer&#x27;,\n",
       "                                                                                               &#x27;Autumn&#x27;,\n",
       "                                                                                               &#x27;Winter&#x27;],\n",
       "                                                                                              [&#x27;VeryLow&#x27;,\n",
       "                                                                                               &#x27;Low&#x27;,\n",
       "                                                                                               &#x27;Medium&#x27;,\n",
       "                                                                                               &#x27;High&#x27;,\n",
       "                                                                                               &#x27;VeryHigh&#x27;]]))]),\n",
       "                                                  [&#x27;season&#x27;,\n",
       "                                                   &#x27;store_traffic_volume&#x27;])])),\n",
       "                (&#x27;regressor&#x27;, RandomForestRegressor())])</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-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;encoder&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;numerical&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputation_median&#x27;,\n",
       "                                                                   SimpleImputer())]),\n",
       "                                                  Index([&#x27;past_sales&#x27;, &#x27;price&#x27;, &#x27;advertising_spend&#x27;, &#x27;discount_rate&#x27;,\n",
       "       &#x27;competitor_sales_volume&#x27;],\n",
       "      dtype=&#x27;object&#x27;)),\n",
       "                                                 (&#x27;ohe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputation_mode&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;onehot&#x27;,\n",
       "                                                                   OneHotEncoder(sparse_output=False))]),\n",
       "                                                  [&#x27;product_category&#x27;,\n",
       "                                                   &#x27;store_location_type&#x27;,\n",
       "                                                   &#x27;promotion_type&#x27;]),\n",
       "                                                 (&#x27;ordinal&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputation_mode&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;ord&#x27;,\n",
       "                                                                   OrdinalEncoder(categories=[[&#x27;Spring&#x27;,\n",
       "                                                                                               &#x27;Summer&#x27;,\n",
       "                                                                                               &#x27;Autumn&#x27;,\n",
       "                                                                                               &#x27;Winter&#x27;],\n",
       "                                                                                              [&#x27;VeryLow&#x27;,\n",
       "                                                                                               &#x27;Low&#x27;,\n",
       "                                                                                               &#x27;Medium&#x27;,\n",
       "                                                                                               &#x27;High&#x27;,\n",
       "                                                                                               &#x27;VeryHigh&#x27;]]))]),\n",
       "                                                  [&#x27;season&#x27;,\n",
       "                                                   &#x27;store_traffic_volume&#x27;])])),\n",
       "                (&#x27;regressor&#x27;, RandomForestRegressor())])</pre></div> </div></div><div class=\"sk-serial\"><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-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;encoder: ColumnTransformer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for encoder: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;numerical&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputation_median&#x27;,\n",
       "                                                  SimpleImputer())]),\n",
       "                                 Index([&#x27;past_sales&#x27;, &#x27;price&#x27;, &#x27;advertising_spend&#x27;, &#x27;discount_rate&#x27;,\n",
       "       &#x27;competitor_sales_volume&#x27;],\n",
       "      dtype=&#x27;object&#x27;)),\n",
       "                                (&#x27;ohe&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputation_mode&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;onehot&#x27;,\n",
       "                                                  OneHotEncoder(sparse_output=False))]),\n",
       "                                 [&#x27;product_category&#x27;, &#x27;store_location_type&#x27;,\n",
       "                                  &#x27;promotion_type&#x27;]),\n",
       "                                (&#x27;ordinal&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputation_mode&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;ord&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;Spring&#x27;,\n",
       "                                                                              &#x27;Summer&#x27;,\n",
       "                                                                              &#x27;Autumn&#x27;,\n",
       "                                                                              &#x27;Winter&#x27;],\n",
       "                                                                             [&#x27;VeryLow&#x27;,\n",
       "                                                                              &#x27;Low&#x27;,\n",
       "                                                                              &#x27;Medium&#x27;,\n",
       "                                                                              &#x27;High&#x27;,\n",
       "                                                                              &#x27;VeryHigh&#x27;]]))]),\n",
       "                                 [&#x27;season&#x27;, &#x27;store_traffic_volume&#x27;])])</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-42\" type=\"checkbox\" ><label for=\"sk-estimator-id-42\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">numerical</label><div class=\"sk-toggleable__content \"><pre>Index([&#x27;past_sales&#x27;, &#x27;price&#x27;, &#x27;advertising_spend&#x27;, &#x27;discount_rate&#x27;,\n",
       "       &#x27;competitor_sales_volume&#x27;],\n",
       "      dtype=&#x27;object&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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-43\" type=\"checkbox\" ><label for=\"sk-estimator-id-43\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content \"><pre>SimpleImputer()</pre></div> </div></div></div></div></div></div></div><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-44\" type=\"checkbox\" ><label for=\"sk-estimator-id-44\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">ohe</label><div class=\"sk-toggleable__content \"><pre>[&#x27;product_category&#x27;, &#x27;store_location_type&#x27;, &#x27;promotion_type&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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-45\" type=\"checkbox\" ><label for=\"sk-estimator-id-45\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content \"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-46\" type=\"checkbox\" ><label for=\"sk-estimator-id-46\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content \"><pre>OneHotEncoder(sparse_output=False)</pre></div> </div></div></div></div></div></div></div><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-47\" type=\"checkbox\" ><label for=\"sk-estimator-id-47\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">ordinal</label><div class=\"sk-toggleable__content \"><pre>[&#x27;season&#x27;, &#x27;store_traffic_volume&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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-48\" type=\"checkbox\" ><label for=\"sk-estimator-id-48\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content \"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-49\" type=\"checkbox\" ><label for=\"sk-estimator-id-49\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;OrdinalEncoder<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></label><div class=\"sk-toggleable__content \"><pre>OrdinalEncoder(categories=[[&#x27;Spring&#x27;, &#x27;Summer&#x27;, &#x27;Autumn&#x27;, &#x27;Winter&#x27;],\n",
       "                           [&#x27;VeryLow&#x27;, &#x27;Low&#x27;, &#x27;Medium&#x27;, &#x27;High&#x27;, &#x27;VeryHigh&#x27;]])</pre></div> </div></div></div></div></div></div></div><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-50\" type=\"checkbox\" ><label for=\"sk-estimator-id-50\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">remainder</label><div class=\"sk-toggleable__content \"><pre></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-51\" type=\"checkbox\" ><label for=\"sk-estimator-id-51\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">passthrough</label><div class=\"sk-toggleable__content \"><pre>passthrough</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-52\" type=\"checkbox\" ><label for=\"sk-estimator-id-52\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a></label><div class=\"sk-toggleable__content \"><pre>RandomForestRegressor()</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('encoder',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('numerical',\n",
       "                                                  Pipeline(steps=[('imputation_median',\n",
       "                                                                   SimpleImputer())]),\n",
       "                                                  Index(['past_sales', 'price', 'advertising_spend', 'discount_rate',\n",
       "       'competitor_sales_volume'],\n",
       "      dtype='object')),\n",
       "                                                 ('ohe',\n",
       "                                                  Pipeline(steps=[('imputation_mode',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('onehot',\n",
       "                                                                   OneHotEncoder(sparse_output=False))]),\n",
       "                                                  ['product_category',\n",
       "                                                   'store_location_type',\n",
       "                                                   'promotion_type']),\n",
       "                                                 ('ordinal',\n",
       "                                                  Pipeline(steps=[('imputation_mode',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('ord',\n",
       "                                                                   OrdinalEncoder(categories=[['Spring',\n",
       "                                                                                               'Summer',\n",
       "                                                                                               'Autumn',\n",
       "                                                                                               'Winter'],\n",
       "                                                                                              ['VeryLow',\n",
       "                                                                                               'Low',\n",
       "                                                                                               'Medium',\n",
       "                                                                                               'High',\n",
       "                                                                                               'VeryHigh']]))]),\n",
       "                                                  ['season',\n",
       "                                                   'store_traffic_volume'])])),\n",
       "                ('regressor', RandomForestRegressor())])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_preprocessor = Pipeline([ \n",
    "    (\"imputation_median\", SimpleImputer(strategy=\"mean\"))\n",
    "])\n",
    "\n",
    "ohe_preprocessor = Pipeline([\n",
    "        (\"imputation_mode\", SimpleImputer( strategy=\"most_frequent\")),\n",
    "        (\"onehot\", OneHotEncoder(sparse_output=False ))\n",
    "    ])\n",
    "\n",
    "ord_preprocessor = Pipeline([\n",
    "      (\"imputation_mode\", SimpleImputer( strategy=\"most_frequent\")),\n",
    "        (\"ord\", OrdinalEncoder(categories=categories ))\n",
    "    ])\n",
    "\n",
    "\n",
    "preprocessor = ColumnTransformer([\n",
    "        (\"numerical\", numeric_preprocessor, numerical),\n",
    "        (\"ohe\", ohe_preprocessor, ohe_cols),\n",
    "        (\"ordinal\", ord_preprocessor, ord_cols)\n",
    "    ], remainder='passthrough') #this step is necessary, otherwise the numerical variables are discarded.\n",
    "\n",
    "pipe = Pipeline([\n",
    "     ('encoder', preprocessor),\n",
    "    ('regressor', RandomForestRegressor())\n",
    "   ])\n",
    "pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'regressor__max_depth': 9}"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid_rf = [{'regressor__max_depth': range(7, 10) }  ]\n",
    " \n",
    "    \n",
    "search_rf = GridSearchCV( pipe, param_grid = param_grid_rf, cv=5, scoring='neg_root_mean_squared_error', n_jobs=-1) \n",
    "search_rf.fit(X_train, y_train)\n",
    "\n",
    "model_rf=search_rf.best_estimator_\n",
    "search_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-645.8897315794815"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_rf.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-641.9652110146275"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "641.9652110146275"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import root_mean_squared_error\n",
    "y_pred=model_rf.predict(X_test)\n",
    "RMSE = root_mean_squared_error(y_test, y_pred)\n",
    "RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>past_sales</th>\n",
       "      <th>price</th>\n",
       "      <th>season</th>\n",
       "      <th>product_category</th>\n",
       "      <th>store_location_type</th>\n",
       "      <th>advertising_spend</th>\n",
       "      <th>discount_rate</th>\n",
       "      <th>competitor_sales_volume</th>\n",
       "      <th>promotion_type</th>\n",
       "      <th>store_traffic_volume</th>\n",
       "      <th>sales_volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3712.019206</td>\n",
       "      <td>61.946333</td>\n",
       "      <td>Summer</td>\n",
       "      <td>shoes</td>\n",
       "      <td>suburban</td>\n",
       "      <td>682.836888</td>\n",
       "      <td>28.070782</td>\n",
       "      <td>4198.959000</td>\n",
       "      <td>percentage_off</td>\n",
       "      <td>Low</td>\n",
       "      <td>4964.299973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4666.499402</td>\n",
       "      <td>47.336866</td>\n",
       "      <td>Spring</td>\n",
       "      <td>electronics</td>\n",
       "      <td>downtown</td>\n",
       "      <td>572.218433</td>\n",
       "      <td>17.405044</td>\n",
       "      <td>6576.442539</td>\n",
       "      <td>percentage_off</td>\n",
       "      <td>Medium</td>\n",
       "      <td>4006.739512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6085.299988</td>\n",
       "      <td>54.793826</td>\n",
       "      <td>Spring</td>\n",
       "      <td>cosmetics</td>\n",
       "      <td>downtown</td>\n",
       "      <td>466.107517</td>\n",
       "      <td>17.719031</td>\n",
       "      <td>4641.982873</td>\n",
       "      <td>percentage_off</td>\n",
       "      <td>Low</td>\n",
       "      <td>4295.756331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6102.391928</td>\n",
       "      <td>94.596118</td>\n",
       "      <td>Spring</td>\n",
       "      <td>clothing</td>\n",
       "      <td>downtown</td>\n",
       "      <td>388.514092</td>\n",
       "      <td>22.095615</td>\n",
       "      <td>9055.830815</td>\n",
       "      <td>loyalty_points</td>\n",
       "      <td>Low</td>\n",
       "      <td>6978.062512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3014.237193</td>\n",
       "      <td>65.518271</td>\n",
       "      <td>Summer</td>\n",
       "      <td>cosmetics</td>\n",
       "      <td>suburban</td>\n",
       "      <td>559.756395</td>\n",
       "      <td>15.494180</td>\n",
       "      <td>5762.883494</td>\n",
       "      <td>buy_one_get_one</td>\n",
       "      <td>Low</td>\n",
       "      <td>3499.905963</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    past_sales      price  season product_category store_location_type  \\\n",
       "0  3712.019206  61.946333  Summer            shoes            suburban   \n",
       "1  4666.499402  47.336866  Spring      electronics            downtown   \n",
       "2  6085.299988  54.793826  Spring        cosmetics            downtown   \n",
       "3  6102.391928  94.596118  Spring         clothing            downtown   \n",
       "4  3014.237193  65.518271  Summer        cosmetics            suburban   \n",
       "\n",
       "   advertising_spend  discount_rate  competitor_sales_volume   promotion_type  \\\n",
       "0         682.836888      28.070782              4198.959000   percentage_off   \n",
       "1         572.218433      17.405044              6576.442539   percentage_off   \n",
       "2         466.107517      17.719031              4641.982873   percentage_off   \n",
       "3         388.514092      22.095615              9055.830815   loyalty_points   \n",
       "4         559.756395      15.494180              5762.883494  buy_one_get_one   \n",
       "\n",
       "  store_traffic_volume  sales_volume  \n",
       "0                  Low   4964.299973  \n",
       "1               Medium   4006.739512  \n",
       "2                  Low   4295.756331  \n",
       "3                  Low   6978.062512  \n",
       "4                  Low   3499.905963  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('Question_2.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sales_level\n",
       "1    1500\n",
       "0     981\n",
       "2     519\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sales_level']=1\n",
    "mask=df.sales_volume<4500\n",
    "df.loc[mask, 'sales_level']=0\n",
    "\n",
    "mask=df.sales_volume>6500\n",
    "df.loc[mask, 'sales_level']=2\n",
    "df.sales_level.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(columns='sales_volume')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2400, 10)\n",
      "(600, 10)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df.drop(columns='sales_level'), df['sales_level'], stratify= df['sales_level'], random_state=0, test_size=0.2)\n",
    "\n",
    "print(X_train.shape)\n",
    "\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;encoder&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;numerical&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputation_median&#x27;,\n",
       "                                                                   SimpleImputer())]),\n",
       "                                                  Index([&#x27;past_sales&#x27;, &#x27;price&#x27;, &#x27;advertising_spend&#x27;, &#x27;discount_rate&#x27;,\n",
       "       &#x27;competitor_sales_volume&#x27;],\n",
       "      dtype=&#x27;object&#x27;)),\n",
       "                                                 (&#x27;ohe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputation_mode&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;oneho...\n",
       "                               feature_types=None, gamma=None, grow_policy=None,\n",
       "                               importance_type=None,\n",
       "                               interaction_constraints=None, learning_rate=None,\n",
       "                               max_bin=None, max_cat_threshold=None,\n",
       "                               max_cat_to_onehot=None, max_delta_step=None,\n",
       "                               max_depth=None, max_leaves=None,\n",
       "                               min_child_weight=None, missing=nan,\n",
       "                               monotone_constraints=None, multi_strategy=None,\n",
       "                               n_estimators=None, n_jobs=None,\n",
       "                               num_parallel_tree=None, random_state=None, ...))])</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-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;encoder&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;numerical&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputation_median&#x27;,\n",
       "                                                                   SimpleImputer())]),\n",
       "                                                  Index([&#x27;past_sales&#x27;, &#x27;price&#x27;, &#x27;advertising_spend&#x27;, &#x27;discount_rate&#x27;,\n",
       "       &#x27;competitor_sales_volume&#x27;],\n",
       "      dtype=&#x27;object&#x27;)),\n",
       "                                                 (&#x27;ohe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputation_mode&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;oneho...\n",
       "                               feature_types=None, gamma=None, grow_policy=None,\n",
       "                               importance_type=None,\n",
       "                               interaction_constraints=None, learning_rate=None,\n",
       "                               max_bin=None, max_cat_threshold=None,\n",
       "                               max_cat_to_onehot=None, max_delta_step=None,\n",
       "                               max_depth=None, max_leaves=None,\n",
       "                               min_child_weight=None, missing=nan,\n",
       "                               monotone_constraints=None, multi_strategy=None,\n",
       "                               n_estimators=None, n_jobs=None,\n",
       "                               num_parallel_tree=None, random_state=None, ...))])</pre></div> </div></div><div class=\"sk-serial\"><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-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;encoder: ColumnTransformer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for encoder: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;numerical&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputation_median&#x27;,\n",
       "                                                  SimpleImputer())]),\n",
       "                                 Index([&#x27;past_sales&#x27;, &#x27;price&#x27;, &#x27;advertising_spend&#x27;, &#x27;discount_rate&#x27;,\n",
       "       &#x27;competitor_sales_volume&#x27;],\n",
       "      dtype=&#x27;object&#x27;)),\n",
       "                                (&#x27;ohe&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputation_mode&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;onehot&#x27;,\n",
       "                                                  OneHotEncoder(sparse_output=False))]),\n",
       "                                 [&#x27;product_category&#x27;, &#x27;store_location_type&#x27;,\n",
       "                                  &#x27;promotion_type&#x27;]),\n",
       "                                (&#x27;ordinal&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputation_mode&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;ord&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;Spring&#x27;,\n",
       "                                                                              &#x27;Summer&#x27;,\n",
       "                                                                              &#x27;Autumn&#x27;,\n",
       "                                                                              &#x27;Winter&#x27;],\n",
       "                                                                             [&#x27;VeryLow&#x27;,\n",
       "                                                                              &#x27;Low&#x27;,\n",
       "                                                                              &#x27;Medium&#x27;,\n",
       "                                                                              &#x27;High&#x27;,\n",
       "                                                                              &#x27;VeryHigh&#x27;]]))]),\n",
       "                                 [&#x27;season&#x27;, &#x27;store_traffic_volume&#x27;])])</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-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">numerical</label><div class=\"sk-toggleable__content \"><pre>Index([&#x27;past_sales&#x27;, &#x27;price&#x27;, &#x27;advertising_spend&#x27;, &#x27;discount_rate&#x27;,\n",
       "       &#x27;competitor_sales_volume&#x27;],\n",
       "      dtype=&#x27;object&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content \"><pre>SimpleImputer()</pre></div> </div></div></div></div></div></div></div><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-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">ohe</label><div class=\"sk-toggleable__content \"><pre>[&#x27;product_category&#x27;, &#x27;store_location_type&#x27;, &#x27;promotion_type&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content \"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content \"><pre>OneHotEncoder(sparse_output=False)</pre></div> </div></div></div></div></div></div></div><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-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">ordinal</label><div class=\"sk-toggleable__content \"><pre>[&#x27;season&#x27;, &#x27;store_traffic_volume&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content \"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;OrdinalEncoder<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></label><div class=\"sk-toggleable__content \"><pre>OrdinalEncoder(categories=[[&#x27;Spring&#x27;, &#x27;Summer&#x27;, &#x27;Autumn&#x27;, &#x27;Winter&#x27;],\n",
       "                           [&#x27;VeryLow&#x27;, &#x27;Low&#x27;, &#x27;Medium&#x27;, &#x27;High&#x27;, &#x27;VeryHigh&#x27;]])</pre></div> </div></div></div></div></div></div></div><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-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">remainder</label><div class=\"sk-toggleable__content \"><pre></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-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">passthrough</label><div class=\"sk-toggleable__content \"><pre>passthrough</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">XGBClassifier</label><div class=\"sk-toggleable__content \"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('encoder',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('numerical',\n",
       "                                                  Pipeline(steps=[('imputation_median',\n",
       "                                                                   SimpleImputer())]),\n",
       "                                                  Index(['past_sales', 'price', 'advertising_spend', 'discount_rate',\n",
       "       'competitor_sales_volume'],\n",
       "      dtype='object')),\n",
       "                                                 ('ohe',\n",
       "                                                  Pipeline(steps=[('imputation_mode',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('oneho...\n",
       "                               feature_types=None, gamma=None, grow_policy=None,\n",
       "                               importance_type=None,\n",
       "                               interaction_constraints=None, learning_rate=None,\n",
       "                               max_bin=None, max_cat_threshold=None,\n",
       "                               max_cat_to_onehot=None, max_delta_step=None,\n",
       "                               max_depth=None, max_leaves=None,\n",
       "                               min_child_weight=None, missing=nan,\n",
       "                               monotone_constraints=None, multi_strategy=None,\n",
       "                               n_estimators=None, n_jobs=None,\n",
       "                               num_parallel_tree=None, random_state=None, ...))])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe = Pipeline([\n",
    "     ('encoder', preprocessor),\n",
    "    ('classifier', XGBClassifier())\n",
    "   ])\n",
    "pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'classifier__n_estimators': 300}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "param_grid_rf = [{'classifier__n_estimators': range(100, 400, 100) }  ]\n",
    " \n",
    "    \n",
    "search_xg = GridSearchCV( pipe, param_grid = param_grid_rf, cv=5, scoring='f1_macro', n_jobs=-1) \n",
    "search_xg.fit(X_train, y_train)\n",
    "\n",
    "model_xg=search_xg.best_estimator_\n",
    "search_xg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8068939277512788"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_xg.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8198694997691057"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_xg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=model_xg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,  ConfusionMatrixDisplay\n",
    "cm=confusion_matrix(y_test, y_pred)\n",
    "\n",
    "ConfusionMatrixDisplay(cm).plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    - 0: if below 4500 (low level), \n",
    "    - 1: if equal or larger than 4500 and less than 6500 (medium level)\n",
    "    - 2: if equal or larger than 6500 (high level)\n",
    "\n",
    "Regarding model B, the sales team would like to know what percentage of all high sales the model wrongly identifies as medium sales, out of all the high sales."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- high sales wrongly identified as medium sales: predicted as 1, true 2 : 27\n",
    "- total high sales: 27+77"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.961538461538463"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "27/(27+77)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 3\n",
    "\n",
    "**7 points**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You are hired by the Customer support department to develop a model that sorts customer reviews into those about Clothing and those about Electronics. The reviews stored in a file: `Question_3.csv` with the following information:\n",
    "\n",
    "- `Review`\n",
    "- `Category`\n",
    "\n",
    "\n",
    "The goal is to have develop an approach that distinguishes between these two categories of reviews and evaluate the performance of your approach. Check whether it is more useful to use only single words or two consecutive words as features, and use only those features that appear in at least two reviews.\n",
    "Additionally, the team has asked you to:\n",
    "- Additionally state the percentage of correctly identified clothing reviews (out of all clothing reviews), and the percentage of correctly identified electronics reviews (out of all electronic reviews).\n",
    "- Show the top 5 tokens associated with each of the categories.\n",
    "- State the number of features used by your model.\n",
    "- Write one sentence review on clothing and show the probability that your model gives to that review for each of the categories.\n",
    "  \n",
    "You need to justify your decisions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>Review</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Perfect for my needs.</td>\n",
       "      <td>Electronics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Very comfortable and stylish.</td>\n",
       "      <td>Clothing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Not good.</td>\n",
       "      <td>Clothing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Exceeded my expectations.</td>\n",
       "      <td>Electronics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amazing durability.</td>\n",
       "      <td>Electronics</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Review     Category\n",
       "0          Perfect for my needs.  Electronics\n",
       "1  Very comfortable and stylish.     Clothing\n",
       "2                      Not good.     Clothing\n",
       "3      Exceeded my expectations.  Electronics\n",
       "4            Amazing durability.  Electronics"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('Question_3.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(323, 2)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category\n",
       "Clothing       173\n",
       "Electronics    150\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Category.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category\n",
       "0    173\n",
       "1    150\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapping={'Clothing':0, 'Electronics':1}\n",
    "df['Category']=df['Category'].replace(mapping)\n",
    "df.Category.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Review      0\n",
       "Category    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check for duplicates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>Review</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Review, Category]\n",
       "Index: []"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df['Review'], df['Category'], \n",
    "                                                    stratify= df['Category'],  test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((258,), (65,))"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category\n",
       "0    138\n",
       "1    120\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best cross-validation score:  0.616289592760181\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    " \n",
    "\n",
    "pipe = Pipeline ([ ('tokenizer', CountVectorizer(min_df=2) ),\n",
    "                   ('classifier', LogisticRegression(solver='liblinear'))\n",
    "                  ] )\n",
    "param_grid = {\n",
    "               'tokenizer__ngram_range': [(1, 1), (1, 2)]}\n",
    "\n",
    "grid = GridSearchCV(pipe, param_grid, cv=5, n_jobs=-1)\n",
    "grid.fit(X_train, y_train)\n",
    "print('Best cross-validation score: ', grid.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "115"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer = grid.best_estimator_.named_steps['tokenizer']\n",
    "len(vectorizer.vocabulary_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_tokenizer__ngram_range</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.009803</td>\n",
       "      <td>0.000757</td>\n",
       "      <td>0.002813</td>\n",
       "      <td>0.000512</td>\n",
       "      <td>(1, 1)</td>\n",
       "      <td>{'tokenizer__ngram_range': (1, 1)}</td>\n",
       "      <td>0.576923</td>\n",
       "      <td>0.653846</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.616290</td>\n",
       "      <td>0.030643</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.009509</td>\n",
       "      <td>0.000450</td>\n",
       "      <td>0.002400</td>\n",
       "      <td>0.000491</td>\n",
       "      <td>(1, 2)</td>\n",
       "      <td>{'tokenizer__ngram_range': (1, 2)}</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.673077</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.607843</td>\n",
       "      <td>0.568627</td>\n",
       "      <td>0.600679</td>\n",
       "      <td>0.045623</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       0.009803      0.000757         0.002813        0.000512   \n",
       "1       0.009509      0.000450         0.002400        0.000491   \n",
       "\n",
       "  param_tokenizer__ngram_range                              params  \\\n",
       "0                       (1, 1)  {'tokenizer__ngram_range': (1, 1)}   \n",
       "1                       (1, 2)  {'tokenizer__ngram_range': (1, 2)}   \n",
       "\n",
       "   split0_test_score  split1_test_score  split2_test_score  split3_test_score  \\\n",
       "0           0.576923           0.653846           0.615385           0.647059   \n",
       "1           0.538462           0.673077           0.615385           0.607843   \n",
       "\n",
       "   split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "0           0.588235         0.616290        0.030643                1  \n",
       "1           0.568627         0.600679        0.045623                2  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(grid.cv_results_ ).sort_values(by='mean_test_score', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred=grid.predict(X_test)\n",
    "cm=confusion_matrix(y_test, y_pred)\n",
    "\n",
    "ConfusionMatrixDisplay(cm).plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "{'Clothing':Negative, 'Electronics':Positive}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_names=np.array(vectorizer.get_feature_names_out())\n",
    "coef = grid.best_estimator_.named_steps['classifier'].coef_.ravel()\n",
    "idx_positive_coefficients = np.argsort(coef)[-5:]\n",
    "idx_negative_coefficients = np.argsort(coef)[:5]\n",
    "idx_interesting_coefficients = np.hstack([idx_negative_coefficients, idx_positive_coefficients])\n",
    "\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "plt.bar(feature_names[idx_interesting_coefficients], coef[idx_interesting_coefficients])\n",
    "plt.xticks(rotation=90, ha=\"right\") ; "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.69      0.57      0.62        35\n",
      "           1       0.58      0.70      0.64        30\n",
      "\n",
      "    accuracy                           0.63        65\n",
      "   macro avg       0.64      0.64      0.63        65\n",
      "weighted avg       0.64      0.63      0.63        65\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.87360927, 0.12639073]])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.predict_proba( ['Cheap fabric'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_transformed = grid.best_estimator_.named_steps['tokenizer'].transform(X_train)\n",
    "from sklearn.manifold import TSNE\n",
    "tsne = TSNE(random_state=42,  perplexity = 30,  init=\"random\")\n",
    "X_tsne = tsne.fit_transform(X_transformed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 't-SNE feature 1')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6))\n",
    "plt.scatter(X_tsne[:, 0], X_tsne[:, 1],  s=30, c=y_train)\n",
    "plt.xlabel(\"t-SNE feature 0\")\n",
    "plt.ylabel(\"t-SNE feature 1\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.7"
  },
  "toc-showcode": false,
  "toc-showmarkdowntxt": false
 },
 "nbformat": 4,
 "nbformat_minor": 4
}