Customer_Segmentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Neuron_Labs.png](Neuron_Labs.png)\n",
    "\n",
    "<H1><center>Week-3 Task\n",
    "<h3><center>Customer Segmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> Importing all required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Reading Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541904</th>\n",
       "      <td>581587</td>\n",
       "      <td>22613</td>\n",
       "      <td>PACK OF 20 SPACEBOY NAPKINS</td>\n",
       "      <td>12</td>\n",
       "      <td>12/9/2011 12:50</td>\n",
       "      <td>0.85</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541905</th>\n",
       "      <td>581587</td>\n",
       "      <td>22899</td>\n",
       "      <td>CHILDREN'S APRON DOLLY GIRL</td>\n",
       "      <td>6</td>\n",
       "      <td>12/9/2011 12:50</td>\n",
       "      <td>2.10</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541906</th>\n",
       "      <td>581587</td>\n",
       "      <td>23254</td>\n",
       "      <td>CHILDRENS CUTLERY DOLLY GIRL</td>\n",
       "      <td>4</td>\n",
       "      <td>12/9/2011 12:50</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541907</th>\n",
       "      <td>581587</td>\n",
       "      <td>23255</td>\n",
       "      <td>CHILDRENS CUTLERY CIRCUS PARADE</td>\n",
       "      <td>4</td>\n",
       "      <td>12/9/2011 12:50</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541908</th>\n",
       "      <td>581587</td>\n",
       "      <td>22138</td>\n",
       "      <td>BAKING SET 9 PIECE RETROSPOT</td>\n",
       "      <td>3</td>\n",
       "      <td>12/9/2011 12:50</td>\n",
       "      <td>4.95</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>541909 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0         536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1         536365     71053                  WHITE METAL LANTERN         6   \n",
       "2         536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3         536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4         536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "...          ...       ...                                  ...       ...   \n",
       "541904    581587     22613          PACK OF 20 SPACEBOY NAPKINS        12   \n",
       "541905    581587     22899         CHILDREN'S APRON DOLLY GIRL          6   \n",
       "541906    581587     23254        CHILDRENS CUTLERY DOLLY GIRL          4   \n",
       "541907    581587     23255      CHILDRENS CUTLERY CIRCUS PARADE         4   \n",
       "541908    581587     22138        BAKING SET 9 PIECE RETROSPOT          3   \n",
       "\n",
       "            InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0        12/1/2010 8:26       2.55     17850.0  United Kingdom  \n",
       "1        12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
       "2        12/1/2010 8:26       2.75     17850.0  United Kingdom  \n",
       "3        12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
       "4        12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
       "...                 ...        ...         ...             ...  \n",
       "541904  12/9/2011 12:50       0.85     12680.0          France  \n",
       "541905  12/9/2011 12:50       2.10     12680.0          France  \n",
       "541906  12/9/2011 12:50       4.15     12680.0          France  \n",
       "541907  12/9/2011 12:50       4.15     12680.0          France  \n",
       "541908  12/9/2011 12:50       4.95     12680.0          France  \n",
       "\n",
       "[541909 rows x 8 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_data = pd.read_csv('Customers.csv', encoding='latin-1')\n",
    "customers_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 541909 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count   Dtype  \n",
      "---  ------       --------------   -----  \n",
      " 0   InvoiceNo    541909 non-null  object \n",
      " 1   StockCode    541909 non-null  object \n",
      " 2   Description  540455 non-null  object \n",
      " 3   Quantity     541909 non-null  int64  \n",
      " 4   InvoiceDate  541909 non-null  object \n",
      " 5   UnitPrice    541909 non-null  float64\n",
      " 6   CustomerID   406829 non-null  float64\n",
      " 7   Country      541909 non-null  object \n",
      "dtypes: float64(2), int64(1), object(5)\n",
      "memory usage: 33.1+ MB\n"
     ]
    }
   ],
   "source": [
    "customers_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Labelling the Country Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
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       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541904</th>\n",
       "      <td>581587</td>\n",
       "      <td>22613</td>\n",
       "      <td>PACK OF 20 SPACEBOY NAPKINS</td>\n",
       "      <td>12</td>\n",
       "      <td>12/9/2011 12:50</td>\n",
       "      <td>0.85</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541905</th>\n",
       "      <td>581587</td>\n",
       "      <td>22899</td>\n",
       "      <td>CHILDREN'S APRON DOLLY GIRL</td>\n",
       "      <td>6</td>\n",
       "      <td>12/9/2011 12:50</td>\n",
       "      <td>2.10</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541906</th>\n",
       "      <td>581587</td>\n",
       "      <td>23254</td>\n",
       "      <td>CHILDRENS CUTLERY DOLLY GIRL</td>\n",
       "      <td>4</td>\n",
       "      <td>12/9/2011 12:50</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541907</th>\n",
       "      <td>581587</td>\n",
       "      <td>23255</td>\n",
       "      <td>CHILDRENS CUTLERY CIRCUS PARADE</td>\n",
       "      <td>4</td>\n",
       "      <td>12/9/2011 12:50</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541908</th>\n",
       "      <td>581587</td>\n",
       "      <td>22138</td>\n",
       "      <td>BAKING SET 9 PIECE RETROSPOT</td>\n",
       "      <td>3</td>\n",
       "      <td>12/9/2011 12:50</td>\n",
       "      <td>4.95</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>541909 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0         536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1         536365     71053                  WHITE METAL LANTERN         6   \n",
       "2         536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3         536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4         536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "...          ...       ...                                  ...       ...   \n",
       "541904    581587     22613          PACK OF 20 SPACEBOY NAPKINS        12   \n",
       "541905    581587     22899         CHILDREN'S APRON DOLLY GIRL          6   \n",
       "541906    581587     23254        CHILDRENS CUTLERY DOLLY GIRL          4   \n",
       "541907    581587     23255      CHILDRENS CUTLERY CIRCUS PARADE         4   \n",
       "541908    581587     22138        BAKING SET 9 PIECE RETROSPOT          3   \n",
       "\n",
       "            InvoiceDate  UnitPrice  CustomerID Country  \n",
       "0        12/1/2010 8:26       2.55     17850.0       0  \n",
       "1        12/1/2010 8:26       3.39     17850.0       0  \n",
       "2        12/1/2010 8:26       2.75     17850.0       0  \n",
       "3        12/1/2010 8:26       3.39     17850.0       0  \n",
       "4        12/1/2010 8:26       3.39     17850.0       0  \n",
       "...                 ...        ...         ...     ...  \n",
       "541904  12/9/2011 12:50       0.85     12680.0       1  \n",
       "541905  12/9/2011 12:50       2.10     12680.0       1  \n",
       "541906  12/9/2011 12:50       4.15     12680.0       1  \n",
       "541907  12/9/2011 12:50       4.15     12680.0       1  \n",
       "541908  12/9/2011 12:50       4.95     12680.0       1  \n",
       "\n",
       "[541909 rows x 8 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = 0\n",
    "for country in customers_data['Country'].unique():\n",
    "    customers_data.loc[customers_data['Country'] == country, 'Country'] = i\n",
    "    i += 1\n",
    "customers_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Future Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = customers_data[['Quantity','UnitPrice']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Finding the Optimal K value for K-Means clustering\n",
    "<h3>By using Elbow Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[30846411619.78331,\n",
       " 18798329694.037025,\n",
       " 6756267117.561111,\n",
       " 3823754014.889214,\n",
       " 2890756714.1895285,\n",
       " 2359572128.8189,\n",
       " 1942876320.3451943,\n",
       " 1560996301.306553,\n",
       " 1316082281.3495498,\n",
       " 1118688712.7772732]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wcss = []\n",
    "for k in range(1,11):\n",
    "    kmeans = KMeans(n_clusters=k, init=\"k-means++\")\n",
    "    kmeans.fit(X)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "plt.figure(figsize=(12,6))    \n",
    "plt.grid()\n",
    "plt.plot(range(1,11),wcss, linewidth=2, color=\"red\", marker =\"8\")\n",
    "plt.xlabel(\"K Value\")\n",
    "plt.xticks(np.arange(1,11,1))\n",
    "plt.ylabel(\"WCSS\")\n",
    "wcss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>K-Means Clustering "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/0f/p7w7p9gd2n58tm6mb2jlgvj80000gn/T/ipykernel_12450/2697792300.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X['Clusters'] = clusters\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    541862\n",
       "1        42\n",
       "3         2\n",
       "2         2\n",
       "4         1\n",
       "Name: Clusters, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Kyso49NBDufXWW7c56iftig44sj9nfvtovjD5GM789tEGPEmtJgpfGqFalZWVqaqqapv6008/zUEHHVSCHqm53FeSpPYiIh5LKVU2NM8jeZIkSTlkyJMkScohQ54kSVIOGfIkSZJyyJAnSZKUQ4Y8SZKkHDLkSZIk5ZAhT5IkKYcMeXpbZ511FuPHjy91NyRJUjMZ8nLolltu4f3vfz89e/akU6dOpe5OnSFDhjT61WqSJKllGfJyqFevXpx33nlcc801pe5Ki9u0aVOpuyBJ0i7BkNea5s6Aq98Jl1cUfs6dUZTNHHfccXzyk59k2LBh29V+7dq1TJgwgWHDhlFWVsbBBx/MQw891GDbiODhhx+um/7jH/+4xdHCW265hYMOOoiysjL69evHmWeeCcAJJ5zASy+9xPjx4+nRowejR48GYPPmzXz729/mgAMOoKKigqOPPpr63x181lln8alPfYqzzjqL3r1786UvfanZvw9JktqjtnMuL+/mzoCZX4JNGwrTq18uTAMMP7V0/QLGjRvH0qVLefDBBxkyZAjPPffcDq1n/fr1nHHGGdx///0cc8wxrFu3jscffxyAmTNnMmTIEK688ko+/elP1y1z2WWX8cADD3DfffcxePBgbrzxRsaMGcOCBQvo1asXALfeeiu/+MUvuOGGG3jzzTd3fsCSJLUDHslrLQ9e8VbAq7VpQ6FeQsuXL2fGjBlMnjyZoUOHEhHst99+7Lfffju0vs6dO/PMM8/w2muvsfvuu/P+97+/0bYpJSZNmsT3v/99hg0bRseOHRk3bhwDBgzgnnvuqWs3cuRIPvGJT9CxY0e6d+++Q/2SJKm9MeS1ltWLm1cvkh49etS9pk+fzqJFiwA44IADdnrd3bt359577+W+++5j33335YgjjuDmm29utP3KlStZu3YtJ5xwAhUVFXWv559/nsWL3/q9DBkyZKf7JklSe+Pp2tZSvlfhFG1D9Va0du3aLaaXL18OwIIFCzj44IObXL5Hjx6sW7eubnrp0qVbzB81ahSjRo2iurqau+66i49//OMceeSR7LvvvnTosOX/Kfbcc0923313HnjgAd797nc3us2tl5MkSU3zX8/Wcuyl0LnblrXO3Qr1FlZdXc0bb7zBxo0bAXjjjTd44403SClt07Zv376ccsopnHfeeSxatIiUEgsXLmThwoUNrvuII45g2rRpbNy4kUWLFvHDH/6wbt4///lPbrvtNlavXk3Hjh2pqKgAoGPHjgD079+fBQsW1LWPCM4//3wmTJhQV1+7di3333//NuFRkiQ1jyGvtQw/FU6YBOV7A1H4ecKkotx08Ytf/IJu3bpx3HHHUV1dTbdu3ejWrRsvvvhig+2nTp3K4Ycfzr//+79TVlbG2LFjeeWVVxpse91117Fw4UJ69+7NqaeeyllnnVU3r6amhh/96EcMGTKEsrIyvvCFLzBt2rS6063f+MY3uOmmm+jVqxcf/vCHAfjWt77F2LFjGTt2LD179mT//fdn8uTJ1NTUtOjvRJKk9iYaOrrTnlVWVqb6j/Co9fTTT3PQQQeVoEdqLveVJKm9iIjHUkqVDc3zSJ4kSVIOGfIkSZJyyJAnSZKUQ4Y8SZKkHDLkSZIk5ZAhT5IkKYcMeZIkSTlkyJMkScohQ54kSVIOGfLUpFGjRnHllVeWuhuSJKkZDHk59LWvfY1DDjmEnj17MnDgQM455xxee+21UncLgIjg4YcfLnU3JEnKPUNeDnXs2JGbbrqJV199lSeffJLFixdz1llnlbpbLWbTpk2l7oIkSW2eIa8V3fP8PYz+9WiGTxvO6F+P5p7n7ynKdr797W/zrne9i86dO9OnTx/OP/98/vjHP77tMitWrGDcuHHss88+9OzZkxEjRjB//vxt2i1atIiIYPHixXW1G2+8kf32269uetKkSQwdOpSysjIGDRrEJZdcAsBhhx0GwOjRo+nRowfjx48HYP369UyYMIGhQ4fSu3dvxowZw8KFC+vWN2rUKC644AJOOukkevbsyVVXXbXDvxtJktqLTqXuQHtxz/P3cPlfLueN6jcAWLZuGZf/5XIAPjrso0Xd9oMPPlgXsBpSU1PDiSeeyMCBA5kzZw59+vTh73//O2VlZc3e1rPPPstFF13EnDlzOOSQQ1i1ahXPPPMMAE8++SQRwaxZsxg5cmTdMueccw6rV6/mkUceoVevXkycOJHjjz+eefPm0blzZwCmTp3KHXfcwe23386GDRua3S9JktobQ14rufbxa+sCXq03qt/g2sevLWrIu+2225g8eTJ/+tOfGm1TVVVFVVUVK1eupLy8HIDhw4fv0PY6depESomnnnqKwYMHU1FRwVFHHdVo+5UrV3LzzTfz4osv0q9fPwAuu+wyrrnmGmbPnl0XBk855RSOOeYYALp3775DfZMkqT3xdG0reWXdK82qt4Rbb72Vc845h7vuuosRI0YA8NJLL9GjR4+610MPPcSiRYvo27dvXcDbGcOGDWP69OlMmTKFgQMHMnLkSGbNmtVo+xdeeAEohMqKigoqKiro3bs3mzZt4uWXX65rN2TIkJ3umyRJ7YlH8lpJ/937s2zdsgbrxfCzn/2Mr3zlK8ycOZOjjz66rr7PPvuwdu3aLdo++uijLF++nDVr1tCzZ8+3XW/tKdx169bV1ZYuXbpFm5NPPpmTTz6ZjRs3MnnyZMaOHcurr75K9+7diYgt2g4ePBiABQsW0KdPn0a326GD/x+RJKk5/JezlZw/4ny6duy6Ra1rx66cP+L8Ft/WpEmTmDBhAvfff/8WAa8xlZWVjBgxgvHjx7N8+XJqamqYO3fuNuENYI899mDw4MFMnTqV6upq5s2bx5QpU+rmz58/n/vuu4/169fTuXNnysvLiYi6kNa/f38WLFhQ175v376cfvrpnHfeeSxZsgSAVatWcfvtt28TRiVJ0vYz5LWSjw77KJe/73IG7D6AIBiw+wAuf9/lRbke7/zzz2fNmjV84AMf2OLUbGM6dOjAzJkz6datG4cffjgVFRWcffbZjYasadOmcffdd1NeXs6FF17IuHHj6uZt3LiRK664ggEDBlBRUcGkSZO47bbb6Nq1EHAnTpzIpZdeSq9evTj33HMBmDJlCgceeCCjRo2irKyMQw89lFtvvXWbo36SJGn7RUqp1H1oUyorK1NVVdU29aeffpqDDjqoBD1Sc7mvJEntRUQ8llKqbGieR/IkSZJyyJAnSZKUQ4Y8SZKkHDLkSZIk5ZAhT5IkKYcMeZIkSTlkyJMkScohQ54kSVIOGfLUpFGjRnHllVeWuhuSJKkZDHk59fWvf52hQ4fSs2dP+vbtyymnnMJLL71U6m4RETz88MOl7oYkSblnyMupM844gyeeeII1a9awaNEi9tlnH0477bRSd6tFbNq0qdRdkCSpzTPktaLVM2ey4Jhjefqgg1lwzLGsnjmzaNt6xzveQXl5OQApJTp06MD8+fMbbb9ixQrGjRvHPvvsQ8+ePRkxYkSD7RctWkREsHjx4rrajTfeyH777Vc3PWnSJIYOHUpZWRmDBg3ikksuAeCwww4DYPTo0fTo0YPx48cDsH79eiZMmMDQoUPp3bs3Y8aMYeHChXXrGzVqFBdccAEnnXQSPXv25KqrrtqJ34wkSe1Dp1J3oL1YPXMmy755KemNNwDYvHQpy755KQDlJ5xQlG3efPPN/Od//idr1qyhU6dO/PCHP2ywXU1NDSeeeCIDBw5kzpw59OnTh7///e+UlZU1e5vPPvssF110EXPmzOGQQw5h1apVPPPMMwA8+eSTRASzZs1i5MiRdcucc845rF69mkceeYRevXoxceJEjj/+eObNm0fnzp0BmDp1KnfccQe33347GzZs2IHfhiRJ7YtH8lrJ8quvqQt4tdIbb7D86muKts3TTz+d1atXs2zZMi6//HIOPfTQBttVVVVRVVXF1KlT6devHx06dGD48OEMHDiw2dvs1KkTKSWeeuop1q5dS0VFBUcddVSj7VeuXMnNN9/M9ddfT79+/ejSpQuXXXYZy5YtY/bs2XXtTjnlFI455hgigu7duze7X5IktTclC3kR0TUiHo2IJyPiqYj4VlYfGhGzI2JhRPwqIrpk9d2y6YXZ/CH11nVxVp8fEcfVq4/Jagsj4qJWH2Q9m5cta1a9JfXv359zzjmH448/npdeeokePXrUvR566CEWLVpE3759607v7oxhw4Yxffp0pkyZwsCBAxk5ciSzZs1qtP0LL7wAwPDhw6moqKCiooLevXuzadMmXn755bp2Q4YM2em+SZLUnpTydO2bwDEppbUR0Rl4OCJ+C1wIXJ1SuiUiJgPjgB9nP19PKe0XEacB3wU+EREHA6cBhwADgQci4oBsGz8CPgQsBuZExF0ppX+05iBrdRowgM1LlzZYbw2bN29m3bp1rFmzhrVr124x79FHH2X58uWsWbOGnj17vu16ak/hrlu3rq62dKtxnXzyyZx88sls3LiRyZMnM3bsWF599VW6d+9ORGzRdvDgwQAsWLCAPn36NLrdDh086CxJUnOU7F/OVFCbNjpnrwQcA/w6q08DTsrej82myeYfG4XEMBa4JaX0ZkrpBWAh8J7stTCl9HxKaSNwS9a2JPp++QKia9ctatG1K32/fEGLb6umpobrrruO5cuXA7B48WK+8IUvMGTIEN7xjnds076yspIRI0Ywfvx4li9fTk1NDXPnzt0mvAHsscceDB48mKlTp1JdXc28efOYMmVK3fz58+dz3333sX79ejp37kx5eTkRURfS+vfvz4IFC+ra9+3bl9NPP53zzjuPJUuWALBq1Spuv/32bcKoJEnafiU9PBIRHSPiCWA58DvgOWBVSmlz1mQxMCh7Pwh4GSCbvxrYo359q2UaqzfUj89FRFVEVK1YsaIFRrat8hNOYMB/X0GngQMhgk4DBzLgv68o2k0X9957L+985zvZfffdOfLII+nevTsPPPAAnTpte/C2Q4cOzJw5k27dunH44YdTUVHB2Wef3WjImjZtGnfffTfl5eVceOGFjBs3rm7exo0bueKKKxgwYAAVFRVMmjSJ2267ja5ZwJ04cSKXXnopvXr14txzzwVgypQpHHjggYwaNYqysjIOPfRQbr311m2O+kmSpO0XKaVS94GIqABuB74J3JhS2i+r7w38NqX0zoj4OzAmpbQ4m/cccCRwOfBISummrH4D8Nts1WNSSuOz+hnAkSmlL75dXyorK1NVVdU29aeffpqDDjpoZ4eqVuC+kiS1FxHxWEqpsqF5beJCp5TSKuAPwHuBioioPdy0F7Ake78E2Bsgm18OvFq/vtUyjdUlSZJyr5R31/bJjuAREd0o3CDxNIWwd0rW7Ezgzuz9Xdk02fzfp8JhyLuA07K7b4cC+wOPAnOA/bO7dbtQuDnjrqIPTJIkqQ0o5d21A4BpEdGRQtickVK6OyL+AdwSEVcCfwNuyNrfAPwiIhYCr1EIbaSUnoqIGcA/gM3AF1JK1QAR8UXgfqAjMDWl9FTrDU+SJKl0ShbyUkpzgXc1UH+ewp2xW9ffAP6jkXVNBCY2UL8XuHenOytJkrSLaRPX5EmSJKllGfIkSZJyyJAnSZKUQ4Y8SZKkHDLkSZIk5ZAhT9tt1KhRXHnllaXuhiRJ2g6GvJyrqanhfe97HxHB4sWL37btAw88wOjRo6moqKC8vJxDDjmESy+9lNWrV7d4vyKChx9+uMXXK0mSCgx5OXf11VfTvXv3JtvdeOONnHDCCYwePZr58+ezevVq7r77bv71r38xd+7cVuhp823atKnUXZAkqc0y5LWiZ2e/wrRL/syPPv97pl3yZ56d/Upxt/fss1x//fX84Ac/eNt2a9eu5YILLuDiiy9mwoQJ9OvXD4ChQ4dy9dVX8/73v3+bZRYtWrTN0cEbb7yR/fbbr2560qRJDB06lLKyMgYNGsQll1wCwGGHHQbA6NGj6dGjB+PHjwdg/fr1TJgwgaFDh9K7d2/GjBnDwoUL69Y3atQoLrjgAk466SR69uzJVVddtYO/GUmS8q+UX2vWrjw7+xX+MP0ZNm+sAWDta2/yh+nPAHDAkf1bfHs1NTWcffbZ/OAHP6CiouJt2/7lL39h9erVnH766S22/WeffZaLLrqIOXPmcMghh7Bq1SqeeaYw3ieffJKIYNasWYwcObJumXPOOYfVq1fzyCOP0KtXLyZOnMjxxx/PvHnz6Ny5MwBTp07ljjvu4Pbbb2fDhg0t1l9JkvLGI3mt5K93PlcX8Gpt3ljDX+98rijbu/baa+nfvz8f+9jHmmy7YsUKAAYNGtRi2+/UqRMpJZ566inWrl1LRUUFRx11VKPtV65cyc0338z1119Pv3796NKlC5dddhnLli1j9uzZde1OOeUUjjnmGCJiu05DS5LUXhnyWsna195sVn1nLFy4kKuuuorrrrtum3kvvfQSPXr0qHs99NBD9OnTB4AlS5a0WB+GDRvG9OnTmTJlCgMHDmTkyJHMmjWr0fYvvPACAMOHD6eiooKKigp69+7Npk2bePnll+vaDRkypMX6KElSnnm6tpX06L1bg4GuR+/dWnxbDz/8MCtWrOCd73wnUDh1C4UAdeWVV7J27dot2q9du5by8nJ++ctf8s1vfnO7tlFWVgbAunXr6mpLly7dos3JJ5/MySefzMaNG5k8eTJjx47l1VdfpXv37kTEFm0HDx4MwIIFC+pCZ0M6dPD/JZIkbQ//xWwl7x27L526bPnr7tSlA+8du2+Lb+vUU0/lueee44knnuCJJ57g3nvvBWDWrFl85jOf2aZ9jx49uPrqq/mf//kfrr766rrTty+++CITJkzgoYce2maZPfbYg8GDBzN16lSqq6uZN28eU6ZMqZs/f/587rvvPtavX0/nzp0pLy8nIupCWv/+/VmwYEFd+759+3L66adz3nnn1R1RXLVqFbfffvs2oVSSJDXNkNdKDjiyPx/41Dvqjtz16L0bH/jUO4py00X37t3Za6+96l79+xe20b9/f3r06NHgMp/97Ge58847uffee9lvv/0oLy/nIx/5CLvvvnvd3bBbmzZtGnfffTfl5eVceOGFjBs3rm7exo0bueKKKxgwYAAVFRVMmjSJ2267ja5duwIwceJELr30Unr16sW5554LwJQpUzjwwAMZNWoUZWVlHHroodx6663bHPWTJElNi5RSqfvQplRWVqaqqqpt6k8//TQHHXRQCXqk5nJfSZLai4h4LKVU2dA8j+RJkiTlkCFPkiQphwx5kiRJOWTIkyRJyiFDXjPUPm9ObZf7SJKkAkPedtp9991ZsmQJGzduxDuS256UEhs3bmTJkiXsvvvupe6OJEkl5zdebKe99tqLlStX8uKLL7J58+ZSd0cN6NSpE+Xl5ey5556l7ookSSVnyNtOHTp0oG/fvvTt27fUXZEkSWqSp2slSZJyyJAnSZKUQ4Y8SZKkHDLkSZIk5ZAhT5IkKYcMeZIkSTlkyJMkScohQ54kSVIOGfIkSZJyyJAnSZKUQ4Y8SZKkHDLkSZIk5ZAhT5IkKYcMeZIkSTlkyJMkScohQ54kSVIOGfIkSZJyyJAnSZKUQ4Y8SZKkHDLkSZIk5ZAhT5IkKYcMeZIkSTlkyJMkScohQ54kSVIOGfIkSZJyyJAnSZKUQ4Y8SZKkHDLkSZIk5ZAhT5IkKYcMeZIkSTlkyJMkScohQ54kSVIOGfIkSZJyyJAnSZKUQ4Y8SZKkHDLkSZIk5VDJQl5E7B0Rf4iIf0TEUxFxflbvHRG/i4gF2c9eWT0iYlJELIyIuRExot66zszaL4iIM+vVj4iIedkykyIiWn+kkiRJra+UR/I2A19JKR0MHAV8ISIOBi4CHkwp7Q88mE0DfBjYP3t9DvgxFEIhcBlwJPAe4LLaYJi1OafecmNaYVySJEklV7KQl1JallJ6PHv/L+BpYBAwFpiWNZsGnJS9Hwv8PBU8AlRExADgOOB3KaXXUkqvA78DxmTzeqaUHkkpJeDn9dYlSZKUa23imryIGAK8C5gN9EspLctmvQL0y94PAl6ut9jirPZ29cUN1Bva/ucioioiqlasWLFzg5EkSWoDSh7yIqIHcBtwQUppTf152RG4VOw+pJR+mlKqTClV9unTp9ibkyRJKrqShryI6Ewh4E1PKf0mK/8zO9VK9nN5Vl8C7F1v8b2y2tvV92qgLkmSlHulvLs2gBuAp1NKP6w36y6g9g7ZM4E769U/k91lexSwOjutez8wOiJ6ZTdcjAbuz+atiYijsm19pt66JEmScq1TCbd9NHAGMC8inshqlwDfAWZExDjgReDUbN69wEeAhcB64LMAKaXXIuK/gTlZuytSSq9l788DbgS6Ab/NXpIkSbkXhcveVKuysjJVVVWVuhuSJElNiojHUkqVDc0r+Y0XkiRJanmGPEmSpBwy5EmSJOWQIU+SJCmHDHmSJEk5ZMiTJEnKIUOeJBXT3Blw9Tvh8orCz7kzSt0jSe1EKR+GLEn5NncGzPwSbNpQmF79cmEaYPipjS8nSS3AI3mSVCwPXvFWwKu1aUOhLklFZsiTpGJZvbh5dUlqQYY8SSqW8r2aV5ekFmTIk6RiOfZS6Nxty1rnboW6JBWZIU+SimX4qXDY6RAdC9PRsTDtTReSWoEhT5KKZe4MePJmSNWF6VRdmPYxKpJagSFPkorFu2sllZAhT5KKxbtrJZXQdoe8iBgZEZ/N3veJiKHF65Yk5UC3Xs2rS1IL2q6QFxGXAV8DLs5KnYGbitUpSZIk7ZztPZL3MeBEYB1ASmkpUFasTklSLmx4vXl1SWpB2xvyNqaUEpAAImL34nVJknLChyFLKqHtDXkzIuInQEVEnAM8AEwpXrckKQd8GLKkEuq0PY1SSj+IiA8Ba4ADgUtTSr8ras8kaVdX+9DjB68o3FFbvlch4PkwZEmtYLtCXnYn7UO1wS4iukXEkJTSomJ2TpJ2ecNPNdRJKontPV17K1BTb7o6q0mSJKkN2t6Q1ymltLF2InvfpThdkiRJ0s7a3pC3IiJOrJ2IiLHAyuJ0SZIkSTtru67JAz4PTI+I64AAXgY+U7ReSZIkaads7921zwFHRUSPbHptUXslSZKknfK2IS8iPp1SuikiLtyqDkBK6YdF7JskSZJ2UFNH8mq/2cKvMJMkSdqFvG3ISyn9JCI6AmtSSle3Up8kSZK0k5q8uzalVA18shX6IkmSpBayvXfX/jm7s/ZXwLraYkrp8aL0SpIkSTtle0Pe4dnPK+rVEnBMi/ZGkiRJLWJ7Q95/pJR8+LEkSdIuoqlHqJwATAU2RUQNcGpK6S+t0jNJyoNpJ8ILf3preui/w5l3la4/ktqNpm68mAi8P6U0EPg48D/F75Ik5cTWAQ8K09NObLi9JLWgpkLe5pTSMwAppdn4vDxJ2n5bB7ym6pLUgpq6Jq/vVt92scW033ghSZLUNjUV8qaw5dG7+tOpKD2SJEnSTmvqGy++BRARR6eU/lx/XkQcXcyOSdIur2MXqN7YcF2SiqzJb7zI/N/trEmSao39ERBbFSOrS1JxNfUIlfcC7wP6bHVtXk+gYzE7Jkm7vOGnFn4+eAWsXgzle8Gxl75Vl6QiauqavC5Aj6xd/Wvz1gCnFKtTkpQbw0811EkqiaauyfsT8KeIuDGl9GIr9UmSJEk7qanTtdeklC4ArouIbe6mTSn5RE9JkqQ2qKnTtb/Ifv6g2B2RJElSy2nqdO1j2U8fzy5JkrQLaepIHlD3TLzLgcHZMgGklNKw4nVNkiRJO2q7Qh5wA/Bl4DGgunjdkaScmTvDR6hIKontDXmrU0q/LWpPJClv5s6AmV+CTRsK06tfLkyDQU9S0W3vN178ISK+HxHvjYgRta+i9kySdnUPXvFWwKu1aUOhLklFtr1H8o7Mfh6R/QwgAce0eI8kKS9WL25eXZJaUFPPyav9KrO7s58JWAE8nFJ6oZgdk6RdXvlehVO0DdUlqciaOl1blr16ZK8yoBL4bUScVuS+SdKubf/RzatLUgtq6jl532qoHhG9gQeAW4rRKUnKhQWzmleXpBa0vTdebCGl9BqF6/IkSY3xmjxJJbRDIS8iPgC83sJ9kaR8aezaO6/Jk9QKmrrxYh6Fmy3q6w0sBT5TrE5JUi4ce+mWz8kD6NytUJekImvqESrHbzWdgFdTSuuK1B9Jyo/aBx77jReSSqCpGy9eLObGI2IqhSC5PKX0zqzWG/gVMARYBJyaUno9IgK4FvgIsB44K6X0eLbMmcA3stVemVKaltWPAG4EugH3AuenlLY+MilJxTP8VEOdpJLYoWvyWtCNwJitahcBD6aU9gcezKYBPgzsn70+B/wY6kLhZRQe2Pwe4LKI6JUt82PgnHrLbb0tSZKkXCppyEsp/T/gta3KY4Fp2ftpwEn16j9PBY8AFRExADgO+F1K6bWU0uvA74Ax2byeKaVHsqN3P6+3LkmSpFwr9ZG8hvRLKS3L3r8C9MveDwLqPzp+cVZ7u/riBurbiIjPRURVRFStWLFi50cgSZJUYm0x5NXJjsAV/Rq6lNJPU0qVKaXKPn36FHtzkiRJRdcWQ94/s1OtZD+XZ/UlwN712u2V1d6uvlcDdUmSpNxriyHvLuDM7P2ZwJ316p+JgqOA1dlp3fuB0RHRK7vhYjRwfzZvTUQcld2Z+5l665IkScq1pp6TV1QR8UtgFLBnRCymcJfsd4AZETEOeBGoffbAvRQen7KQwiNUPguFr1iLiP8G5mTtrsi+dg3gPN56hMpvs5ckSVLuhY+N21JlZWWqqqoqdTckSZKaFBGPpZQqG5rXFk/XSpIkaScZ8iRJknLIkCdJkpRDhjxJKqa5M+Dqd8LlFYWfc2eUukeS2omS3l0rSbk2dwbc8XmoqS5Mr365MA0w/NTGl5OkFuCRPEkqlrsveCvg1aqpLtQlqcgMeZJULBvXNa8uSS3IkCdJkpRDhjxJKppoZl2SWo4hT5KKpfLs5tUlqQUZ8iSpWPY5Cjp03LLWoWOhLklFZsiTpGJ58IqG76598IrS9EdSu2LIk6RiWb24eXVJakGGPEkqli7dm1eXpBZkyJOkYtm4vnl1SWpBhjxJKprUzLoktRxDniRJUg4Z8iRJknLIkCdJkpRDhjxJKpYOXZpXl6QWZMiTpGLpvkfz6pLUggx5klQsa5c1ry5JLciQJ0mSlEOGPEmSpBwy5EmSJOWQIU+SJCmHDHmSJEk5ZMiTJEnKIUOeJBVLdGxeXZJakCFPkoplyMjm1SWpBRnyJKlYXvxr8+qS1IIMeZJULDUbm1eXpBZkyJMkScohQ54kSVIOGfIkSZJyyJAnSZKUQ4Y8SZKkHDLkSVKx+DBkSSVkyJOkYjniLNJWpZTVJanYDHmSVCTf2PxZfr75g2xOHUgJNqcO/HzzB/nG5s+WumuS2oFOpe6AJOXVzbNfoiadzWWbz96i3mH2S1x50qEl6pWk9sIjeZJUJDVbn6ttoi5JLcmQJ0mSlEOGPEmSpBwy5EmSJOWQIU+SJCmHDHmSJEk5ZMiTJEnKIUOeJElSDhnyJEmScsiQJ0mSlEOGPEmSpBwy5EmSJOWQIU+SJCmHDHmSJEk5ZMiTJEnKIUOeJElSDhnyJEmScsiQJ0mSlEOGPEmSpBzqVOoOFFtEjAGuBToC/5tS+k6p+rJ65kyWX30Nm5cto9OAAfT98gWUn3BCm12vlCffuGMeN89+iZr0Vq1X985cdsIhnPSuQQ0uc8fflvD9++ezZNUGOkZQnRK9uncmJVi1YdNO9WfIRfdsV7sAEtRtf1BFN7563IGN9rk5ase3dNUGBrbgeqVdxT3P38O1j1/LK+teof/u/Tl/xPl8dNhH2+x6myvXIS8iOgI/Aj4ELAbmRMRdKaV/tHZfVs+cybJvXkp64w0ANi9dyrJvXgqwU4GsWOuV8uQbd8zjpkde2qb++vpNfPXXTwJsE27u+NsSLv7NPDZsqgagOqW6ZVpTbSat3f6SVRu4+DfzgG373Bxbj6+l1ivtKu55/h4u/8vlvFFd+Pdz2bplXP6XywF2KpAVa707Iu+na98DLEwpPZ9S2gjcAowtRUeWX31NXRCrld54g+VXX9Mm1yvlyS9nv9zovE3Vie/fP3+b+vfvn18XgNqaDZuqG+xzczQ0vpZYr7SruPbxa+uCWK03qt/g2sevbZPr3RF5D3mDgPp/uy/OaluIiM9FRFVEVK1YsaIoHdm8bFmz6qVer5QntUfBGrN01YbtqrUlO9u/xpZv6+OWWsor615pVr3U690ReQ952yWl9NOUUmVKqbJPnz5F2UanAQOaVS/1eqU86RjxtvMHVnTbrlpbsrP9a2z5tj5uqaX0371/s+qlXu+OyHvIWwLsXW96r6zW6vp++QKia9ctatG1K32/fEGbXK+UJ588cu9G53XuGHz1uAO3qX/1uAPp1rljMbu1w7p17thgn5ujofG1xHqlXcX5I86na8ct//3s2rEr5484v02ud0fk+sYLYA6wf0QMpRDuTgNOL0VHam+CaOm7YIu1XilPrjzpUIBm3V1bWyvW3bXbq1h319Yfn3fXqj2qvQmipe+CLdZ6d0SkJq5V2dVFxEeAayg8QmVqSmni27WvrKxMVVVVrdE1SZKknRIRj6WUKhual/cjeaSU7gXuLXU/JEmSWlPer8mTJElqlwx5kiRJOWTIkyRJyiFDniRJUg4Z8iRJknLIkCdJkpRDhjxJkqQcMuRJkiTlkCFPkiQphwx5kiRJOWTIkyRJyiFDniRJUg4Z8iRJknLIkCdJkpRDhjxJkqQcMuRJkiTlkCFPkiQphwx5kiRJOWTIkyRJyiFDniRJUg4Z8iRJknLIkCdJkpRDhjxJkqQcMuRJkiTlkCFPkiQphwx5kiRJOWTIkyRJyiFDniRJUg4Z8iRJknLIkCdJkpRDhjxJkqQcMuRJkiTlkCFPkiQphwx5kiRJOWTIkyRJyiFDniRJUg4Z8iRJknLIkCdJkpRDhjxJkqQcMuRJkiTlkCFPkiQphwx5kiRJOWTIkyRJyiFDniRJUg4Z8iRJknLIkCdJkpRDhjxJkqQcMuRJkiTlkCFPkiQphwx5kiRJOWTIkyRJyiFDniRJUg4Z8iRJknLIkCdJkpRDhjxJkqQcMuRJkiTlkCFPkiQphwx5kiRJOWTIkyRJyiFDniRJUg6VJORFxH9ExFMRURMRlVvNuzgiFkbE/Ig4rl59TFZbGBEX1asPjYjZWf1XEdElq++WTS/M5g9ptQFKkiSVWKmO5P0dOBn4f/WLEXEwcBpwCDAGuD4iOkZER+BHwIeBg4FPZm0BvgtcnVLaD3gdGJfVxwGvZ/Wrs3aSJEntQklCXkrp6ZTS/AZmjQVuSSm9mVJ6AVgIvCd7LUwpPZ9S2gjcAoyNiACOAX6dLT8NOKneuqZl738NHJu1lyRJyr22dk3eIODletOLs1pj9T2AVSmlzVvVt1hXNn911n4bEfG5iKiKiKoVK1a00FAkSZJKp1OxVhwRDwD9G5j19ZTSncXa7o5IKf0U+ClAZWVlKnF3JEmSdlrRQl5K6YM7sNgSYO9603tlNRqpvwpURESn7Ghd/fa161ocEZ2A8qy9JElS7rW107V3Aadld8YOBfYHHgXmAPtnd9J2oXBzxl0ppQT8ATglW/5M4M566zoze38K8PusvSRJUu6V6hEqH4uIxcB7gXsi4n6AlNJTwAzgH8B9wBdSStXZUbovAvcDTwMzsrYAXwMujIiFFK65uyGr3wDskdUvBOoeuyJJkpR34cGtLVVWVqaqqqpSd0NSTnzjjnn8cvbLVKdExwg+eeTeXHnSoaXulqSciIjHUkqVDc0r2jV5ktTefeOOedz0yEt109Up1U0b9CQVW1u7Jk+ScuOXs19uVl2SWpIhT5KKpLqRy2Eaq0tSSzLkSVKRdGzkS3Yaq0tSSzLkSVKRfPLIvZtVl6SW5I0XklQktTdXeHetpFLwESpb8REqkiRpV/F2j1DxdK0kSVIOGfIkSZJyyJAnSZKUQ4Y8SZKkHDLkSZIk5ZAhT5IkKYcMeZIkSTlkyJMkScohQ54kSVIOGfIkSZJyyJAnSZKUQ4Y8SZKkHIqUUqn70KZExArgxVbc5J7AylbcXlvi2Nuf9jpucOztceztddzQfsdeinEPTin1aWiGIa/EIqIqpVRZ6n6UgmNvf2Nvr+MGx94ex95exw3td+xtbdyerpUkScohQ54kSVIOGfJK76el7kAJOfb2p72OGxx7e9Rexw3td+xtatxekydJkpRDHsmTJEnKIUOeJElSDhnyiiAiDo+IRyLiiYioioj3ZPWIiEkRsTAi5kbEiHrLnBkRC7LXmfXqR0TEvGyZSRERWb13RPwua/+7iOjV+iPdVkT8V0Q8ExFPRcT36tUvzsYwPyKOq1cfk9UWRsRF9epDI2J2Vv9VRHTJ6rtl0wuz+UNadYBNiIivRESKiD2z6fawz7+f7fO5EXF7RFTUm9cu9vvbaWysu5qI2Dsi/hAR/8j+fJ+f1Rv8XLbkZ78tiIiOEfG3iLg7m272Z7W5fx7agoioiIhfZ3/Gn46I97aHfR4RX84+53+PiF9GRNddcp+nlHy18AuYBXw4e/8R4I/13v8WCOAoYHZW7w08n/3slb3vlc17NGsb2bK16/0ecFH2/iLgu21g3B8AHgB2y6b7Zj8PBp4EdgOGAs8BHbPXc8AwoEvW5uBsmRnAadn7ycB/Zu/PAyZn708DflXqcdcb/97A/RQepr1ne9jnWV9GA52y99+t7Vd72e9N/G4aHeuu9gIGACOy92XAs9k+bvBz2ZKf/bbwAi4Ebgbuzqab9VndkT8PbeEFTAPGZ++7ABV53+fAIOAFoFu9fX3WrrjPS/4ByuOLwj/0n8jefxK4OXv/E+CT9drNp/AX5yeBn9Sr/ySrDQCeqVeva1e7bPZ+ADC/DYx7BvDBBuoXAxdv9ft5b/a6f+t22R/2lbwVHOra1S6bve+UtYtSjz3rz6+Bw4BFvBXycr3PG/gdfAyY3p72exO/jwbHWup+tdDY7gQ+1NjnsiU/+6V+AXsBDwLHAHfvyGe1uX8eSj3mrC/lFMJObFXP9T6nEPJephBKO2X7/LhdcZ97urY4LgC+HxEvAz+gsAPhrQ9OrcVZ7e3qixuoA/RLKS3L3r8C9GvB/u+oA4D3Z4er/xQR787qzR33HsCqlNLmrepbrCubvzprX1IRMRZYklJ6cqtZed/nWzubwv/GoR3s9+3Q2Fh3adnpqHcBs2n8c9mSn/1Suwb4P0BNNr0jn9Xm/j7agqHACuBn2anq/42I3cn5Pk8pLaHwb/dLwDIK+/AxdsF93qkYK20PIuIBoH8Ds74OHAt8OaV0W0ScCtwAfLBYfUkppYholWfhNDHuThT+53MU8G5gRkQMa41+tYYmxn4JhdOWraI19zm8/dhTSndmbb4ObAamt1a/1PoiogdwG3BBSmlN/UuoWvtz2Roi4nhgeUrpsYgYVeLutLZOwAjgv1JKsyPiWgqnZ+vkdJ/3AsZSCLmrgFuBMaXs044y5O2glFKjoS0ifg6cn03eCvxv9n4Jheu2au2V1ZYAo7aq/zGr79VAe4B/RsSAlNKyiBgALN+hgTRTE+P+T+A3qXD8+dGIqKHwZc2NjZtG6q8CFRHRKftfUf32tetaHBGdKJxOeHWnB7YdGht7RBxK4S+DJ7N/8PYCHo/CDTe7/D6Ht9/vABFxFnA8cGy2/yEn+30nvd3vYJcTEZ0pBLzpKaXfZOXGPpct+dkvpaOBEyPiI0BXoCdwLc3/rDb3z0NbsBhYnFKanU3/mkLIy/s+/yDwQkppBUBE/IbC52DX2+elPvedxxfwNDAqe38s8Fj2/qNseVHqo1m9N4XrHnplrxeA3tm8rS9K/UhW/z5bXvj6vTYw7s8DV2TvD6BwODqAQ9jy4tPnKVx42il7P5S3Lj49JFv+Vra8wPW87P0X2PIC1xmlHncDv4dFvHVNXq73edaXMcA/gD5b1dvVfm/kd9PoWHe1V/Z5/DlwzVb1Bj+XLfnZbysvCkGl9saLZn1Wd+TPQ1t4AQ8BB2bvL8/2d673OXAk8BTQPevXNOC/dsV9XvIPUB5fwEgK5++fpHDNyhFZPYAfUbirZh5QWW+Zs4GF2euz9eqVwN+zZa7jrW8p2YPChcALKNzR2rsNjLsLcFPW38eBY+rN+3o2hvnUu3uKwt1Yz2bzvl6vPiz7w78w+4NVe8du12x6YTZ/WKnH3cDvYRFvhbxc7/OsXwspBPonstfk9rjf3+b30+BYd7VX9vdaAubW29cfaexz2ZKf/bbyYsuQ1+zPanP/PLSFF3A4UJXt9zsohLTc73PgW8AzWd9+QSGo7XL73K81kyRJyiHvrpUkScohQ54kSVIOGfIkSZJyyJAnSZKUQ4Y8SZKkHDLkSVIDImKviLgzIhZExPMRcV1E7NbC2xgVEe+rN/35iPhM9v6siBjYktuT1L4Y8iRpK1H46pLfAHeklPYH9ge6Ad9r4U2NAupCXkppckrp59nkWYAhT9IO8zl5krSViDgWuCyl9G/1aj2BF4FvAu9IKX0xq98N/CCl9MeI+DGF723uBvw6pXRZ1mYRhafmnwB0Bv4DeAN4BKim8CXw/0XhG3LWUnig9o0UvupoA4UHqp6TUjopW9+HKDxt/2PF+h1I2vV5JE+StnUIhW+tqZNSWkMhfL3dd35/PaVUCQwH/j0ihtebtzKlNAL4MTAhpbSIwlcjXZ1SOjyl9FC9bf2awrcMfCqldDhwL/COiOiTNfksMHXHhyepPTDkSVLLOTUiHgf+RiEoHlxv3m+yn48BQ5qz0lQ45fIL4NMRUQG8l8L3fEpSo97uf6SS1F79AzilfiE7XdsfeBU4oN6srtn8ocAE4N0ppdcj4sbaeZk3s5/V7NjfvT8DZlI4zXtrSmnzDqxDUjvikTxJ2taDQPd6d7p2BK6i8AXqLwCHR0SHiNgbeE+2TE9gHbA6IvoBH96O7fwLKNueeSmlpcBS4BsUAp8kvS1DniRtJTs9+jHglIhYQOHoXU1KaSLwZwpB7x/AJODxbJknKZymfQa4OWvXlJnAxyLiiYh4/1bzbgQmZ/O6ZbXpwMsppad3ZnyS2gfvrpWkJmTPsvsl8LGU0uMl7Md1wN9SSjeUqg+Sdh2GPEnaBUTEYxROB38opfRmU+0lyZAnSZKUQ16TJ0mSlEOGPEmSpBwy5EmSJOWQIU+SJCmHDHmSJEk59P8BePjYchD85S4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmeans=KMeans(n_clusters=5,n_init=10,init=\"k-means++\")\n",
    "clusters = kmeans.fit_predict(X)\n",
    "X['Clusters'] = clusters\n",
    "\n",
    "plt.figure(figsize=(10,7))\n",
    "for cul in X.Clusters.unique():\n",
    "    plt.scatter(X.loc[X.Clusters==cul,'Quantity'],X.loc[X.Clusters==cul,'UnitPrice'])\n",
    "plt.title('Quantity Vs UnitPrice')\n",
    "plt.xlabel('Quantity')\n",
    "plt.ylabel('UnitPrice')\n",
    "plt.legend([\"0-cluster\",\"1-cluster\",\"2-cluster\",\"3-cluster\",\"4-Cluster\"],loc=2,fontsize=13)\n",
    "X.Clusters.value_counts()"
   ]
  }
 ],
 "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": "3.10.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}