P31. Unsupervised ML - DBSCAN Clustering (Wholesale Customers).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unsupervised ML - DBSCAN Clustering (Wholesale Customers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>29703</td>\n",
       "      <td>12051</td>\n",
       "      <td>16027</td>\n",
       "      <td>13135</td>\n",
       "      <td>182</td>\n",
       "      <td>2204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>39228</td>\n",
       "      <td>1431</td>\n",
       "      <td>764</td>\n",
       "      <td>4510</td>\n",
       "      <td>93</td>\n",
       "      <td>2346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>14531</td>\n",
       "      <td>15488</td>\n",
       "      <td>30243</td>\n",
       "      <td>437</td>\n",
       "      <td>14841</td>\n",
       "      <td>1867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>10290</td>\n",
       "      <td>1981</td>\n",
       "      <td>2232</td>\n",
       "      <td>1038</td>\n",
       "      <td>168</td>\n",
       "      <td>2125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2787</td>\n",
       "      <td>1698</td>\n",
       "      <td>2510</td>\n",
       "      <td>65</td>\n",
       "      <td>477</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>440 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Channel  Region  Fresh   Milk  Grocery  Frozen  Detergents_Paper  \\\n",
       "0          2       3  12669   9656     7561     214              2674   \n",
       "1          2       3   7057   9810     9568    1762              3293   \n",
       "2          2       3   6353   8808     7684    2405              3516   \n",
       "3          1       3  13265   1196     4221    6404               507   \n",
       "4          2       3  22615   5410     7198    3915              1777   \n",
       "..       ...     ...    ...    ...      ...     ...               ...   \n",
       "435        1       3  29703  12051    16027   13135               182   \n",
       "436        1       3  39228   1431      764    4510                93   \n",
       "437        2       3  14531  15488    30243     437             14841   \n",
       "438        1       3  10290   1981     2232    1038               168   \n",
       "439        1       3   2787   1698     2510      65               477   \n",
       "\n",
       "     Delicassen  \n",
       "0          1338  \n",
       "1          1776  \n",
       "2          7844  \n",
       "3          1788  \n",
       "4          5185  \n",
       "..          ...  \n",
       "435        2204  \n",
       "436        2346  \n",
       "437        1867  \n",
       "438        2125  \n",
       "439          52  \n",
       "\n",
       "[440 rows x 8 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import Dataset\n",
    "wholesale=pd.read_csv('Database/Wholesale customers data.csv')\n",
    "wholesale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 440 entries, 0 to 439\n",
      "Data columns (total 8 columns):\n",
      " #   Column            Non-Null Count  Dtype\n",
      "---  ------            --------------  -----\n",
      " 0   Channel           440 non-null    int64\n",
      " 1   Region            440 non-null    int64\n",
      " 2   Fresh             440 non-null    int64\n",
      " 3   Milk              440 non-null    int64\n",
      " 4   Grocery           440 non-null    int64\n",
      " 5   Frozen            440 non-null    int64\n",
      " 6   Detergents_Paper  440 non-null    int64\n",
      " 7   Delicassen        440 non-null    int64\n",
      "dtypes: int64(8)\n",
      "memory usage: 27.6 KB\n"
     ]
    }
   ],
   "source": [
    "wholesale.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>29703</td>\n",
       "      <td>12051</td>\n",
       "      <td>16027</td>\n",
       "      <td>13135</td>\n",
       "      <td>182</td>\n",
       "      <td>2204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>39228</td>\n",
       "      <td>1431</td>\n",
       "      <td>764</td>\n",
       "      <td>4510</td>\n",
       "      <td>93</td>\n",
       "      <td>2346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>14531</td>\n",
       "      <td>15488</td>\n",
       "      <td>30243</td>\n",
       "      <td>437</td>\n",
       "      <td>14841</td>\n",
       "      <td>1867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>10290</td>\n",
       "      <td>1981</td>\n",
       "      <td>2232</td>\n",
       "      <td>1038</td>\n",
       "      <td>168</td>\n",
       "      <td>2125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>2787</td>\n",
       "      <td>1698</td>\n",
       "      <td>2510</td>\n",
       "      <td>65</td>\n",
       "      <td>477</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>440 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
       "0    12669   9656     7561     214              2674        1338\n",
       "1     7057   9810     9568    1762              3293        1776\n",
       "2     6353   8808     7684    2405              3516        7844\n",
       "3    13265   1196     4221    6404               507        1788\n",
       "4    22615   5410     7198    3915              1777        5185\n",
       "..     ...    ...      ...     ...               ...         ...\n",
       "435  29703  12051    16027   13135               182        2204\n",
       "436  39228   1431      764    4510                93        2346\n",
       "437  14531  15488    30243     437             14841        1867\n",
       "438  10290   1981     2232    1038               168        2125\n",
       "439   2787   1698     2510      65               477          52\n",
       "\n",
       "[440 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wholesale.drop(['Channel','Region'],axis=1,inplace=True)\n",
    "wholesale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.05293319,  0.52356777, -0.04111489, -0.58936716, -0.04356873,\n",
       "        -0.06633906],\n",
       "       [-0.39130197,  0.54445767,  0.17031835, -0.27013618,  0.08640684,\n",
       "         0.08915105],\n",
       "       [-0.44702926,  0.40853771, -0.0281571 , -0.13753572,  0.13323164,\n",
       "         2.24329255],\n",
       "       ...,\n",
       "       [ 0.20032554,  1.31467078,  2.34838631, -0.54337975,  2.51121768,\n",
       "         0.12145607],\n",
       "       [-0.13538389, -0.51753572, -0.60251388, -0.41944059, -0.56977032,\n",
       "         0.21304614],\n",
       "       [-0.72930698, -0.5559243 , -0.57322717, -0.62009417, -0.50488752,\n",
       "        -0.52286938]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalize heterogenous numerical data using standard scalar fit transform to dataset\n",
    "whole_norm=StandardScaler().fit_transform(wholesale)\n",
    "whole_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DBSCAN(eps=1, min_samples=6)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DBSCAN Clustering\n",
    "dbscan=DBSCAN(eps=1,min_samples=6)\n",
    "dbscan.fit(whole_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  0, -1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "        0,  0,  0,  0,  0, -1, -1, -1,  0,  0,  0, -1,  0,  0,  0,  0,  0,\n",
       "        0,  0,  0,  0,  0, -1, -1,  0,  0,  0,  0, -1,  0, -1,  0, -1,  0,\n",
       "        0,  0,  0,  0,  0, -1,  0,  0,  0,  0, -1,  0,  0,  0, -1,  0,  0,\n",
       "        0,  0,  0, -1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "       -1, -1, -1,  0,  0,  0,  0, -1, -1,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "        0, -1,  0,  0,  0,  0,  0, -1,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "        0,  0,  0,  0,  0,  0, -1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "        0,  0,  0,  0,  0, -1,  0,  0,  0, -1,  0,  0,  0,  0,  0,  0,  0,\n",
       "        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "        0, -1,  0,  0,  0,  0, -1,  0,  0,  0,  0, -1,  0, -1,  0,  0,  0,\n",
       "        0,  0,  0,  0,  0,  0,  0,  0,  0, -1,  0,  0,  0,  0,  0, -1,  0,\n",
       "        0,  0,  0,  0,  0,  0,  0, -1,  0,  0,  0,  0, -1,  0, -1,  0,  0,\n",
       "        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0, -1,  0, -1, -1,\n",
       "        0,  0,  0,  0,  0,  0,  0,  0,  0,  0, -1,  0,  0,  0,  0,  0,  0,\n",
       "        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0, -1,  0,  0,  0,  0,\n",
       "        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "        0,  0,  0,  0,  0,  0, -1,  0,  0,  0,  0,  0,  0, -1,  0,  0,  0,\n",
       "        0,  0, -1,  0,  0,  0,  0,  0,  0,  0, -1,  0,  0,  0,  0, -1,  0,\n",
       "        0,  0,  0, -1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "        0, -1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "        0,  0,  0,  0,  0,  0,  0,  0,  0,  0, -1,  0,  0,  0,  0,  0,  0,\n",
       "        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "        0,  0,  0,  0,  0, -1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "        0,  0, -1,  0,  0,  0,  0,  0,  0,  0, -1,  0, -1,  0,  0],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Noisy samples are given the label -1.\n",
    "dbscan.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>440 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     clusters\n",
       "0           0\n",
       "1           0\n",
       "2          -1\n",
       "3           0\n",
       "4           0\n",
       "..        ...\n",
       "435        -1\n",
       "436         0\n",
       "437        -1\n",
       "438         0\n",
       "439         0\n",
       "\n",
       "[440 rows x 1 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making clusters column (optional step)\n",
    "clusters=pd.DataFrame(dbscan.labels_,columns=['clusters'])\n",
    "clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "      <th>Clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>29703</td>\n",
       "      <td>12051</td>\n",
       "      <td>16027</td>\n",
       "      <td>13135</td>\n",
       "      <td>182</td>\n",
       "      <td>2204</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>39228</td>\n",
       "      <td>1431</td>\n",
       "      <td>764</td>\n",
       "      <td>4510</td>\n",
       "      <td>93</td>\n",
       "      <td>2346</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>14531</td>\n",
       "      <td>15488</td>\n",
       "      <td>30243</td>\n",
       "      <td>437</td>\n",
       "      <td>14841</td>\n",
       "      <td>1867</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>10290</td>\n",
       "      <td>1981</td>\n",
       "      <td>2232</td>\n",
       "      <td>1038</td>\n",
       "      <td>168</td>\n",
       "      <td>2125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>2787</td>\n",
       "      <td>1698</td>\n",
       "      <td>2510</td>\n",
       "      <td>65</td>\n",
       "      <td>477</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>440 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicassen  Clusters\n",
       "0    12669   9656     7561     214              2674        1338         0\n",
       "1     7057   9810     9568    1762              3293        1776         0\n",
       "2     6353   8808     7684    2405              3516        7844        -1\n",
       "3    13265   1196     4221    6404               507        1788         0\n",
       "4    22615   5410     7198    3915              1777        5185         0\n",
       "..     ...    ...      ...     ...               ...         ...       ...\n",
       "435  29703  12051    16027   13135               182        2204        -1\n",
       "436  39228   1431      764    4510                93        2346         0\n",
       "437  14531  15488    30243     437             14841        1867        -1\n",
       "438  10290   1981     2232    1038               168        2125         0\n",
       "439   2787   1698     2510      65               477          52         0\n",
       "\n",
       "[440 rows x 7 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding clusters to dataset\n",
    "wholesale['Clusters']=dbscan.labels_\n",
    "wholesale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}