Lecture20_Customer_Segmentation_Easy_version.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Customer segmentation using clustering an classification (Simple)\n",
    "\n",
    "https://archive.ics.uci.edu/ml/datasets/online+retail : \n",
    "\n",
    "&#51060; &#45936;&#51060;&#53552;&#45716; 2010&#45380; 1&#50900; 20&#51068; &#48512;&#53552; 2011&#45380; 9&#50900; 12&#51068; &#44620;&#51648;&#51032; &#44592;&#44036;&#46041;&#50504; &#50728;&#46972;&#51064; &#51204;&#51088;&#49345;&#44144;&#47000; &#54924;&#49324;&#50640;&#49436; &#48156;&#49373;&#54620; transaction&#50640; &#45824;&#54620; &#45236;&#50857;&#51077;&#45768;&#45796;. &#54644;&#45817; &#54924;&#49324;&#45716; UK-based non-store online &#51204;&#51088;&#49345;&#44144;&#47000; &#54924;&#49324;&#51077;&#45768;&#45796;.\n",
    "\n",
    "&#51060; &#45936;&#51060;&#53552;&#47484; &#51060;&#50857;&#54616;&#50668;, &#49324;&#50857;&#51088;&#51032; &#54665;&#46041;&#51012; &#48516;&#49437;&#54633;&#45768;&#45796;. &#49324;&#50857;&#51088;&#51032; &#54665;&#46041;&#51012; &#48516;&#49437;&#54616;&#50668;, &#49324;&#50857;&#51088;&#51032; &#54665;&#46041;&#51012; &#50696;&#52769;&#54616;&#45716; &#47784;&#45944;&#51012; &#47564;&#46308;&#44192;&#49845;&#45768;&#45796;."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. &#45936;&#51060;&#53552; &#47196;&#46377;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['HR-Employee-Attrition.csv',\n",
       " 'housing.csv',\n",
       " 'click_rates.csv',\n",
       " 'ratings_test.txt',\n",
       " 'selected_reviews',\n",
       " 'winequality-data.csv',\n",
       " 'murders.csv',\n",
       " 'scores.csv',\n",
       " 'ratings_train.txt',\n",
       " 'benz_train.csv',\n",
       " 'e-commerce-data.csv',\n",
       " 'state-population.csv',\n",
       " 'four_sessions.csv',\n",
       " 'superstore.xls',\n",
       " 'web_page_data.csv',\n",
       " 'german_credit_data.csv',\n",
       " 'spam.csv']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "os.listdir(\"./inputs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(541909, 8)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .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>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",
       "  </tbody>\n",
       "</table>\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",
       "      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  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# West Europe countries retail transactions\n",
    "dataset=pd.read_csv(\"./inputs/e-commerce-data.csv\",encoding = \"ISO-8859-1\") \n",
    "print(dataset.shape)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. &#45936;&#51060;&#53552; &#51204;&#52376;&#47532;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['InvoiceNo', 'StockCode', 'Description', 'InvoiceDate', 'Country'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.select_dtypes(include=['object']).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo       object\n",
       "StockCode       object\n",
       "Description     object\n",
       "Quantity         int64\n",
       "InvoiceDate     object\n",
       "UnitPrice      float64\n",
       "CustomerID     float64\n",
       "Country         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Country'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5268\n"
     ]
    }
   ],
   "source": [
    "print(dataset.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(536641, 8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.drop_duplicates(inplace = True)\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(406829, 8)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.dropna(axis = 0, subset =['CustomerID'], inplace = True)\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             0\n",
      "InvoiceNo    0\n",
      "StockCode    0\n",
      "Description  0\n",
      "Quantity     0\n",
      "InvoiceDate  0\n",
      "UnitPrice    0\n",
      "CustomerID   0\n",
      "Country      0\n"
     ]
    }
   ],
   "source": [
    "print(pd.DataFrame(dataset.isnull().sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>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>141</th>\n",
       "      <td>C536379</td>\n",
       "      <td>D</td>\n",
       "      <td>Discount</td>\n",
       "      <td>-1</td>\n",
       "      <td>12/1/2010 9:41</td>\n",
       "      <td>27.50</td>\n",
       "      <td>14527.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>C536383</td>\n",
       "      <td>35004C</td>\n",
       "      <td>SET OF 3 COLOURED  FLYING DUCKS</td>\n",
       "      <td>-1</td>\n",
       "      <td>12/1/2010 9:49</td>\n",
       "      <td>4.65</td>\n",
       "      <td>15311.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>C536391</td>\n",
       "      <td>22556</td>\n",
       "      <td>PLASTERS IN TIN CIRCUS PARADE</td>\n",
       "      <td>-12</td>\n",
       "      <td>12/1/2010 10:24</td>\n",
       "      <td>1.65</td>\n",
       "      <td>17548.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>C536391</td>\n",
       "      <td>21984</td>\n",
       "      <td>PACK OF 12 PINK PAISLEY TISSUES</td>\n",
       "      <td>-24</td>\n",
       "      <td>12/1/2010 10:24</td>\n",
       "      <td>0.29</td>\n",
       "      <td>17548.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>C536391</td>\n",
       "      <td>21983</td>\n",
       "      <td>PACK OF 12 BLUE PAISLEY TISSUES</td>\n",
       "      <td>-24</td>\n",
       "      <td>12/1/2010 10:24</td>\n",
       "      <td>0.29</td>\n",
       "      <td>17548.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    InvoiceNo StockCode                       Description  Quantity  \\\n",
       "141   C536379         D                          Discount        -1   \n",
       "154   C536383    35004C   SET OF 3 COLOURED  FLYING DUCKS        -1   \n",
       "235   C536391     22556    PLASTERS IN TIN CIRCUS PARADE        -12   \n",
       "236   C536391     21984  PACK OF 12 PINK PAISLEY TISSUES        -24   \n",
       "237   C536391     21983  PACK OF 12 BLUE PAISLEY TISSUES        -24   \n",
       "\n",
       "         InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "141   12/1/2010 9:41      27.50     14527.0  United Kingdom  \n",
       "154   12/1/2010 9:49       4.65     15311.0  United Kingdom  \n",
       "235  12/1/2010 10:24       1.65     17548.0  United Kingdom  \n",
       "236  12/1/2010 10:24       0.29     17548.0  United Kingdom  \n",
       "237  12/1/2010 10:24       0.29     17548.0  United Kingdom  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#48152;&#54408;&#51060; &#51080;&#45716; &#44221;&#50864;\n",
    "dataset[(dataset.InvoiceNo).apply(lambda x: 'C' in x)].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(397924, 8)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = dataset[(dataset.InvoiceNo).apply(lambda x: 'C' not in x)]\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4339\n"
     ]
    }
   ],
   "source": [
    "# 4339&#47749;&#51032; transaction&#51012; &#44032;&#51648;&#44256; &#51080;&#45796;.\n",
    "df_customerid_groups=dataset.groupby(\"CustomerID\")\n",
    "print(len(df_customerid_groups.groups))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cluster = pd.DataFrame(columns=['Quantity','UnitPrice','CustomerID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4339, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\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>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>74215.0</td>\n",
       "      <td>1.04</td>\n",
       "      <td>12346.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2458.0</td>\n",
       "      <td>481.21</td>\n",
       "      <td>12347.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2341.0</td>\n",
       "      <td>178.71</td>\n",
       "      <td>12348.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>631.0</td>\n",
       "      <td>605.10</td>\n",
       "      <td>12349.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>197.0</td>\n",
       "      <td>65.30</td>\n",
       "      <td>12350.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Quantity  UnitPrice  CustomerID\n",
       "0   74215.0       1.04     12346.0\n",
       "1    2458.0     481.21     12347.0\n",
       "2    2341.0     178.71     12348.0\n",
       "3     631.0     605.10     12349.0\n",
       "4     197.0      65.30     12350.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count=0\n",
    "for k, v in (df_customerid_groups):\n",
    "    df_cluster.loc[count] = [(v['Quantity'].sum()), v['UnitPrice'].sum(), k]\n",
    "    count+=1\n",
    "print(df_cluster.shape)\n",
    "df_cluster.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Quantity\n",
    "- UnitPrice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.42150e+04, 1.04000e+00],\n",
       "       [2.45800e+03, 4.81210e+02],\n",
       "       [2.34100e+03, 1.78710e+02],\n",
       "       ...,\n",
       "       [1.03000e+02, 6.23900e+01],\n",
       "       [1.39700e+03, 1.22093e+03],\n",
       "       [1.58600e+03, 1.04550e+02]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df_cluster.iloc[:, [0, 1]].values\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc_x = StandardScaler()\n",
    "x = sc_x.fit_transform(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> K-means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# With in cluster sum of squers(Inertia)\n",
    "wcss = []\n",
    "\n",
    "# Random&#54616;&#44172; initial centroid&#47484; &#49440;&#51221;&#54616;&#44256; &#50668;&#47084;&#48264; &#48152;&#48373;&#54616;&#47732; &#51339;&#51008; &#49457;&#45733;&#51012; &#45244; &#49688; &#51080;&#49845;&#45768;&#45796;.\n",
    "for i in range(1,11): \n",
    "    kmeans = KMeans(n_clusters = i, init ='k-means++',max_iter=300,n_init=10)\n",
    "    kmeans.fit(X)\n",
    "    wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'With in cluster sum of squers(WCSS)')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,11) , wcss)\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('Number Of Customer Clusters(customer type clusters)')\n",
    "plt.ylabel('With in cluster sum of squers(WCSS)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> &#49884;&#44033;&#54868;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Elbo &#54252;&#51064;&#53944;&#45716; k=3 &#51077;&#45768;&#45796;.\n",
    "kmeans = KMeans(n_clusters = 3, init = 'k-means++')\n",
    "y_kmeans = kmeans.fit_predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x125745e80>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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a3dsAqrpa4i8EZTQQ8z+eT8mMEqq1mqrtVeRm5zLxuYnMOn0Wo3qPamz1jFaMGZeWTUN+v0HclLf5yY/qKw+0LoSROqGtIUpmlBDaFqJquwtBVrW9itA2J9+8bXMdJRiGYTQ+QQzMwyJyF9BFRM7GLYZzd3rVat2ULSmjWqtjplVrNWWLU1lXyzBaBp9++imjR49m3333ZejQoZSUlPDf//436XL+9a9/sXTp0jRoGIzx48dTVFRE//796dChA0VFRRQVFfHoo9FRtBqGzz77jEMPPZTc3FwmTJiQljqiqbOLTFVvEpEjcIsl7Qdcrqqz065ZK6ZiXUVNyyWaqu1VVK6vzLBGhpEioRCUlUFFBRQUQGkp5KXew66qnHTSSYwZM4aHHnoIgEWLFrF27Vq+853vJFXWv/71L4499lj69++fsj7JsnPnTtq0cfPV77jjDgA++ugjjj32WMrLy9Nad8eOHbnmmmt45513qKzMzDukzhaMuHXA56nqRap6IS5oZZ90K9aaKeheQG527J7I3Oxc+nULPMfVMBqP+fOhVy+YMAFuuMF99url5Ckyd+5csrOzGTduXI2ssLCQQw45hBdffJFjjz22Rn7eeecxbdo0ACZPnkz//v0ZNGgQF154Ia+++iozZ87koosuoqioiOXLl1NeXs7IkSMZNGgQJ510Ehs2uMVhDz30UM4//3yKi4s54IADeOuttzj55JMpKCjgsssuq6nvgQceYPjw4RQVFXHuueeyc+dOADp16sQFF1xAYWEhr732Wp3XuGzZMoYN27Vo5/vvv8/w4W4+en5+PpMmTWLgwIGMGDGCDz/8EIC1a9dy8sknU1xczPDhw3n99d0Xou3UqRMHH3wwOTk5QW93vQnSRfYIu9akBreGtC1+lUZKB5SSJbG/mizJovRAW7PJaOKEQlBS4j6rfGu8qmqXfHNq44iLFy9m6NChSZ2zbt06nnjiCZYsWcK7777LZZddxne/+12OP/54brzxRsrLy9l33335+c9/zvXXX8+7777LwIED+cMf/lBTRrt27ViwYAHjxo3jhBNO4I477mDx4sVMmzaNdevW8f7771NWVsYrr7xCeXk5bdq0YcaMGf6yqxgxYgSLFi1i1Ki6HXT2228/OnTowOLFbg761KlT+cUvflGT3q1bN9577z3OPfdcJk6cCMBvf/tbLr74YhYsWMDDDz/MWWedldQ9ShdBDEzbiPXG8fvt0qeSkdc+j1mnzyKvXV5NSyY3O5e8dk7eqV2nRtbQMOqgrAyqY48jUl3t0jPEN77xDXJychg7diyPP/44HTt23C3Pl19+ycaNG/n+978PwJgxY3j55Zdr0o8//ngABg4cyIABA+jZsyft27dnn332YeXKlcyZM4eFCxcybNgwioqKmDNnTk3rok2bNvz4xz9OSuexY8cydepUduzYwSOPPMJpp51WkxbeP/3003n1Vbd+2wsvvMC4ceMoKirixBNPZMOGDXz11VdJ1ZkOgrgpfy4ix6vqTAAROQH4Ir1qGaN6j2L1BaspW1xG5fpK+nXrR+mBpWZcjOZBRcWulks0VVWQ4hjAgAED4g6Ct23bluoIoxaONtC2bVvefPNN5syZw6OPPspf//pX/vOf/yRVb/v27QHIysqq2Q8f79ixA1VlzJgxXHvttbudm5OTUzPuEpSf/OQn/OlPf+Lggw/moIMOokuXLjVpsdyIVZU333yTdu2a1n//IC2YccClIvKxiKzERT8+N71qGQCd2nVi7JCxXPvDaxk7ZKwZF6P5UFAAuXFmNOTmQr/UxhEPP/xwtm7dypQpU2pk7777LvPmzWPvvfdm6dKlbN26lY0bNzJnzhwANm/ezJdffklJSQl//vOfWbRoEQB5eXmEQiHAtXK6du3KvHnzALj//vtrWjNB+MEPfsCjjz7KZ599BsD69ev53/+Sjm5fQ8eOHTn88MM577zzanWPAZT51t+DDz7IwQe7+e4//OEPa5wGgLQ7DAQliBfZcmCkiHTyxzYJwzCMxJSWgh8f2I2sLJeeAiLCE088wYQJE7j++uvJycmhT58+3Hrrrey1116ceuqpHHjggfTt25fBgwcDEAqFOOGEE/j6669RVW655RYARo8ezdlnn83tt9/Oo48+yvTp0xk3bhxbtmxhn332YerUqYH16t+/P1dffTVHHnkk1dXVZGdnc8cdd7D33nundJ3gusBmzZrFD37wg1ryL774gkGDBtGhQwcefPBBwHmk/epXv6rpVjvssMNqGZww+fn5bNmyhe3bt/Poo48yZ84c9ttvv5R1rIu4schE5AxVfUBEYj4lqnpL2rRKIxaLzDBS5/333+eAAw4Ilnn+fDegX13tusVyc51xmTULAgx2t3auu+46tm7dyhVXXFEjy8/PZ/HixbW6zNJBrO+5oWORhdu3FhbGMIzkGTUKVq92A/qVla5brLQUOllXb10cd9xxrFy5MumxoqZGXAOjqnf5UPubVPXPGdTJMIyWQqdOMHZsY2vR7HjqqadiyletWhVT3lRJOMivqjuB0xLlMQzDMIxYBHFTfkVE/gqUATV+h6r6dtq0MgzDMJo9QQxMkf+8KkKmwOENr45hGIbRUgjipnxYJhQxDMMwWhZxx2BEZISILBKRzSLymogE9E00DMNIHxauPzWeffZZhgwZwsCBAxk6dCgvvvhiWuqJJFEL5g7gQuBl4HjgVuBHadfIMIwWQwNH67dw/fXgW9/6Fv/+97/p2bMnixYt4thjj2XlypVprRNVjbkBbyc6rmsDcoA3gUXAEuAPXt4XeAOoxDkOtPPy9v640qf3iSjrEi9fBvwoQn6Ul1UCk4PoNXToUDUMIzWWLl0aOO+8eap5eaq5uargPvPynDxV5syZo4ccckjMtLlz5+oxxxxTczx+/HidOnWqqqpOmjRJDzjgAB04cKBecMEF+sorr2jXrl21T58+WlhYqJWVlfrOO+/oiBEjdODAgXriiSfq+vXrVVX1+9//vk6YMEGHDh2q+++/v7755pt60kknab9+/fT3v/99TX3333+/Dhs2TAsLC/Wcc87RHTt2qKpqbm6uTpw4UQcNGqTzYlz8ihUrdMCAATXHH3zwgRYXF9ccL126VIcNG6aqqr169dKLL75YDzzwQB0+fLguX75cVVU//fRTPemkk3To0KE6bNgwfe211xLex507d2qXLl1027ZtMdNjfc/AAk3CBqhqQjflLiJycniLcVwXW4HDVbUQ5yhwlIiMBK4H/qyq/YANQNhJfiywwcv/7PMhIv2B0cAAb1D+JiJt/BydO4Cjgf7AaT6vYRiNTJqi9Vu4fhomXP/DDz/MiBEjyM7OTupeJkuiLrKXgOPiHCvweKKCvcULP0bZfgt7n/3Uy6cDVwJ3Aif4fYBHgb+KCxt6AvCQqm4FVohIJTDc56tU1Q8BROQhn7fxOlUNwwCCRevP1PzLyHD9xx57bK1FycLECtf/k5/8pCY9Vrh+oCZc//z582vC9QN89dVXfOtb3wLqF67/+uuv55FHHuGdd96pSYsM1z958mTAhetftmxZTZ5wuP4OHTrsVvZ7773HZZddxuzZ6V+YONFM/l/ESwuKb2UsBPrhWhvLgY2qusNnWQX08vu9gJW+7h0i8iXQ3csjl2eLPGdllHxEHD3OAc4B6N27d/0uyjCMOklTtH4L10/9wvV//PHHnHzyyTzwwAP07ds3KZ1SIUi4/pRR1Z2qWgTk41od+6ezvgR6TFHVYlUt7tGjR2OoYBitijRF67dw/aQern/Dhg0cc8wx3HTTTYwcOTJl3ZIhrQYmjKpuBOYCB+HGcsItp3zgE7//CbAXgE//BrAuUh51Tjy5YRiNTGmpC5wci3pE668J1//CCy+w7777MmDAAC655BK+/e1v1wrXf+qpp9YK13/ssccyaNAgRo0aVStc/4033sjgwYNZvnw506dP56KLLmLQoEGUl5dz+eWXB9YrMlz/oEGDOOKII1izZk1qF+k5/fTTyc7Ojhuu/8477+Tmm28GnEfaK6+8wqBBg+jfvz933333buXddtttrFixgiuuuKLGJXrdunX10rFO6vICANoHkcXI0wPo4vc7APOAY4FHgNFe/nfg135/PPB3vz8aeNjvD8B5orXHeaB9CLTBde996GXtfJ4BdellXmSGkTqN7UXWmrj22mv1yiuvrCXr1auXbtiwIe11N5QXWZBQMa8BQwLIoukJTPfjMFneYDwtIkuBh0TkauAd4B8+/z+A+/0g/npvZFDVJSLyMG7wfgcwXl0QTkTkPOA5b3DuVdUlAa7HMIwMYNH6U6elhOtPtODYt3GD6Q/gvL7CI0udcS2NRhlPqS+24JhhpE5SC44ZzZZMLDj2I+BM3NhG5OqVIeDSZCoxDMMwWh+J3JSn47q4fqyqj2VQJ8MwDKMFENfAiMgZqvoA0EdEJkanq+otMU4zDMMwDCBxF1nYi92G5AzDMIykiTsPRlXv8p9/iLVlTkXDMIxdWLj+1HjttdcoLCykqKiIwsJCnnzyybTUE0mdbsoi0gM4G+gTmV9Vf5k+tQzDaAmEtoYoW1JGxboKCroXUDqglLz2qcfrVwvXnzKFhYUsXLiQtm3bsnr1agYPHsxxxx1HVrwZsQ1AkJKfxM2qfwH4d8RmGIYRl/kfz6fXLb2Y8OwEbnj1BiY8O4Fet/Ri/sfzUy5z7ty5ZGdnM27cuBpZYWEhhxxyCC+++GKtQJbnnXce06ZNA2Dy5Mn079+fQYMGceGFF/Lqq68yc+ZMLrroIoqKili+fDnl5eWMHDmSQYMGcdJJJ7FhwwYADj30UM4//3yKi4s54IADeOuttzj55JMpKCjgsssuq6nvgQceYPjw4RQVFXHuueeyc+dOADp16sQFF1xAYWEhr732Wp3XuGzZspqgmeBchocPd/F98/PzmTRpEgMHDmTEiBF8+OGHAKxdu5aTTz6Z4uJihg8fzuuvv75buR07dqRtW9dG+OqrrwCIN02loQhiYDqq6iRVfVhVHwtvadXKMIxmTWhriJIZJYS2haja7qJeVm2vIrTNyTdvSy1ev4Xrr1+4/ldffZUBAwZQWFjI3XffnXQQzmQJYmCeFpGStGphGEaLomxJGdUaO15/tVZTtrgsY7pEhut//PHH6dix4255YoXrf/nll2vSY4Xrb9++fU24/jlz5tSE6y8qKmLOnDk1rYv6hOvfsWMHjzzySE2Ifqgdrv/VV18FXLj+cePGUVRUxIknnlgTrj+a7373uyxZsoQ33niDa665hm3btiWlV7IECRXzO+BSEdkKbMfN6FdV7ZxWzQzDaLZUrKuoablEU7W9isr1qcXrt3D99QvXH2bAgAG0b9+epUuXUlRUlJRuyVBnC0ZV81Q1S1U7qGpnf2zGxTCMuBR0LyA3O3a8/tzsXPp1Sy1ev4XrTz1c/4oVK2rGhVasWEFFRQV77713yjoGIa6BEZEhUdtgEdkrXn7DMIwwpQNKyZLYr5csyaL0wNTi9Vu4/tTD9b/00ksMGjSIoqIiTjnlFO666y66du1aLx3rIlGwy7kxxN1wofFPU9X0+tSlCQt2aSRDQ7vZNneSCXY5/+P5lMwooVqrqdpeRW52LlmSxazTZzGqd92D3a2d6667jq1bt3LFFVfUyPLz81m8eHGtLrN0kPZgl6p6WCy5iBQDtwPfS6Yiw2huxHpBTnxuor0gAzKq9yhWX7CassVlVK6vpF+3fpQeWEqndhYcpC5aSrj+IIP8tVDVBSJiT4jRool0sw0THrQumVHC6gtW24syAJ3adWLskLGNrUaz46mnnoopX7VqVYY1qR9JT+EUkT2A9M7OMYxGpim52RpGcyVRNOW/sLsh6QZ8F+e6bBgtlnS52RpGayJRF1n0SLgC64CJqvpZ+lQyjMYn7GYby8jUx83WMFoTdS04ZhitktIBpUx8brdlkID6udkaRmsi0TyYp0TkOBHJjpG2j4hcJSIWUdlokeS1z2PW6bPIa5dXM2EwNzuXvHZObgP8QVgO/BrojHvVdPbHy+tdckOF7J82bRqrV69O+rzLL7+cF154YTd5dMDN1k6iLrKzgYnArSKyHvgcyMGF7V8O/FVV4y4o4Cdl3gfaiAYSAAAgAElEQVSEnQKmqOptInKlL/tzn/VSVZ3lz7kEGAvsBH6rqs95+VHAbUAb4B5Vvc7L+wIPAd2BhcDPVDW9wXWMVoO52daHZ4BTcNGltntZCLgHmA48ChydUskNGbJ/2rRpHHjggey55567pUWG1o/mqquuSl7x1oiq1rnhjMpBQBEuunKQc3oCQ/x+HvBfoD9wJXBhjPz9gUVAe6Avzoi18dtyYB/cJM9FQH9/zsPAaL//d+BXdek1dOhQNQwjNZYuXRogV6Wq1vWa6OjzJc+cOXP0kEMOiZl2ww03aHFxsQ4cOFAvv/xyVVVdsWKF7r///nrWWWdp//799YgjjtAtW7boI488orm5ufqd73xHCwsLdcuWLbr33nvrxRdfrIMHD9YHH3xQ33nnHR0xYoQOHDhQTzzxRF2/fr2qqo4ZM0YfeeQRVVV95plndL/99tPBgwfrb37zGz3mmGNUVfXFF1/UwsJCLSws1KKiIt20aVNK19sYxPqegQUa4N0fuQVyU1bVj1T1NVUtV9UtAc9Zo6pv+/0Q8D7QK8EpJwAPqepWVV0BVALD/Vapqh+qa508BJwgLuLb4bi/QuD+Fp0YRDfDMNLJzexqtcRjO/DnlEqPF7L/+eefp6KigjfffJPy8nIWLlxYExG5oqKC8ePHs2TJErp06cJjjz3GKaecQnFxMTNmzKC8vJwOHToA0L17d95++21Gjx6dMIQ/uICaZ599Nk899RQLFy7k008/rUm76aabuOOOOygvL2fevHk15bcm6jQwIhISkU1R20oReUJE9glSiYj0AQYDb3jReSLyrojcKyLhYDi9gJURp63ysnjy7sBGVd0RJY9V/zkiskBEFnz++eexshiG0WA8QDADc3+D1vr888/z/PPPM3jwYIYMGcIHH3xARUUFAH379q2JGjx06FA++uijuOWUljoHjrpC+AN88MEH9O3bl4KCAkSEM844oybt4IMPZuLEidx+++1s3LixZrGv1kSQFsytwEW4l3c+cCHwT1xL4t66Tvaz/h8DJqjqJuBOYF9cd9sa3N+dtKKqU1S1WFWLe/Toke7qDKOVE3QxsdQWHRswYAALFy7cTa6qXHLJJZSXl1NeXk5lZSVjx7ooApEh9tu0acOOHTt2Oz9Mbm7sKNDJMnnyZO655x6++uorDj74YD744IMGKbc5EcTAHK+qd6lqSFU3qeoU4EeqWgYkDMXpPdAeA2ao6uMAqrpWVXeqajVwN64LDOATIDJac76XxZOvA7qISNsouWEYjUpQJ4jUnCXihezv3Lkz9957L5s3O8P1ySef1ITPj0dkyP5ogoTw33///fnoo49Yvtx5xj344IM1acuXL2fgwIFMmjSJYcOGtUoDE6TNtkVETmXXWMcpwNd+P27IGD9G8g/gfVW9JULeU1XDcaxPAhb7/ZnAP0XkFmBPoAB4E7fAWYH3GPsEGA38VFXVR3w+BdeaGgPE9WozDCNTnIHzFkvUTZYN/Cyl0sMh+ydMmMD1119PTk4Offr04dZbb6VLly4cdNBBAHTq1IkHHngg4WJfZ555JuPGjaNDhw689tpru6VPnz6dcePGsWXLFvbZZx+mTp1aKz0nJ4cpU6ZwzDHH0LFjRw455JAag3Xrrbcyd+5csrKyGDBgAEcfnZrXXHMmbrj+mgxunOU2nBeZAq8D5+Ne9kNVdX6c80YB84D3gHBQp0uB03DdYwp8BJwbNjgi8nvgl8AOXJfaM15eguuqawPcq6rXROj2EC6EzTvAGaq6NdH1WLh+w0idYOH6lwODgET+QB2Bd3G95UZTI+3h+sOo6ofAcXGSYxoXf958XOsjmlkJzrkGuCaGfFas87xuw6PlhmE0JvviOjyi58GAa7lk+3QzLi2dOg2MiPTATYzsE5lfVW0Wv2EYcTga10L5M85bbDNuzOVnuA4QMy6tgSBjME/iurpewM2wNwyjFaOquCHWutgX+KvfjOZCXcMmyRDEwHRU1UkNVqNhGM2WnJwc1q1bR/fu3QMaGaM5oaqsW7eOnJycBikviIF5WkRK/DiIYRitmPz8fFatWoVNWG655OTkkJ+f3yBlBTEwvwMuFZGtuNE6AVRVOzeIBoZhNBuys7Pp27dvY6thNBOCeJHlZUIRwzAMo2WRaMnk/VX1AxEZEis9HMjSMAzDMGKRqAVzAc49OVasMMVFMjYMwzCMmCRaMvls/3lY5tQxDMMwWgqJushOTnRiOHilYRiGYcQiURdZvPAw4LrIzMAYhmEYcUnURfYLEckCTlHVhzOok2EYhtECSLgejF+z5eIM6WIYhmG0IIIsOPaCiFwoInuJSLfwlnbNDMMwjGZNkJn8pf5zfIRMgX0aXh3DMAyjpRBkJr/FhTAMwzCSps4uMhHpKCKXicgUf1wgIsemXzXDMAyjORNkDGYqsA34rj/+BLg6bRoZhmEYLYIgBmZfVb0Bv+6pqm4h9lLIhmEYhlFDEAOzTUQ64Ab2EZF9ga1p1cowDMNo9gQxMFcCzwJ7icgMYA5Q5wqX3q15rogsFZElIvI7L+8mIrNFpMJ/dvVyEZHbRaRSRN6NjOIsImN8/goRGRMhHyoi7/lzbpdWusReaGuIe96+h0mzJ3HP2/cQ2hpqbJUMwzCQIOsvi0h3YCSua+x1Vf0iwDk9gZ6q+raI5AELgROBM4H1qnqdiEwGuqrqJBEpAX4DlAAjgNtUdYSfc7MAKMa1ohYCQ1V1g4i8CfwWeAOYBdyuqs8k0qu4uFgXLFhQ5zU3F+Z/PJ+SGSVUazVV26vIzc4lS7KYdfosRvUe1djqGYbRQhCRhapanMw5QbzI5qjqOlX9t6o+rapfiMicus5T1TXhNWNUNQS8D/QCTgCm+2zTcUYHL79PHa8DXbyR+hEwW1XXq+oGYDZwlE/rrKqvq7OS90WU1SoIbQ1RMqOE0LYQVdurAKjaXkVom5Nv3ra5kTU0DKM1E9fAiEiObz18U0S6Rszi74MzFIHx5wzGtTT2UNU1PulTYA+/3wtYGXHaKi9LJF8VQx6r/nNEZIGILGhJa4mXLSmjWqtjplVrNWWLyzKskWEYxi4STbQ8F5gA7InrlgqPb2wC/hq0AhHpBDwGTFDVTZHDJKqqIlJ3H109UdUpwBRwXWTpri9TVKyrqGm5RFO1vYrK9ZUZ1sgwDGMXcVswqnqbn8V/oaruo6p9/VaoqoEMjIhk44zLjIj1Y9b67q3wOM1nXv4JsFfE6flelkieH0PeaijoXkBudm7MtNzsXPp165dhjQzDMHYRxIvsUz9Ij5/R/3ikh1c8vEfXP4D3VfWWiKSZQNgTbAzwZIT8596bbCTwpe9Kew440nfTdQWOBJ7zaZtEZKSv6+cRZbUKSgeUkiWxv8IsyaL0wNKYaYZhGJkgiIH5P1UNicgo4Ic4o3FngPMOBn4GHC4i5X4rAa4DjhCRCl/edT7/LOBDoBK4G/g1gKquB/4IvOW3q7wMn+cef85yIKEHWUsjr30es06fRV67vJqWTG52LnntnLxTu06NrKFhGK2ZOt2UReQdVR0sItcC76nqP8OyzKjYsLQ0N2WAzds2U7a4jMr1lfTr1o/SA0vNuBiG0aCk4qYcJFz/JyJyF3AEcL2ItCdYy8fIEJ3adWLskLGNrYZhGEYtghiKU3HjID9S1Y1AN+CitGplGIZhNHuCrAezBXg84ngNsCb+GYZhGIZhXV2GYRhGmjADYxiGYaQFMzCGYRhGWggS7PJkHyb/SxHZJCIhEdmUCeUMwzCM5ksQN+UbgONU9f10K2MYhmG0HIJ0ka0142IYhmEkS5AWzAIRKQP+RcRSyRHBKw3DMAxjN4IYmM7AFlyQyTBKxNwYwzAMw4gmyETLX2RCEcMwDKNlEdfAiMjFqnqDiPwF12Kphar+Nq2aGYZhGM2aRC2Y8MB+ywo9bBiGYWSEuAZGVZ/yn9Mzp45hGIbRUrCZ/IZhGEZaMANjGIZhpIUgoWIODiIzDMMwjEiCtGD+ElBmGIZhGDUkclM+CPgu0ENEJkYkdQbapFsxwzAMo3mTqAXTDuiEM0J5Edsm4JS6ChaRe0XkMxFZHCG7UkQ+EZFyv5VEpF0iIpUiskxEfhQhP8rLKkVkcoS8r4i84eVlItIumQs3DMMw0ksiN+WXgJdEZJqq/i+FsqcBfwXui5L/WVVvihSISH9gNDAA2BN4QUS+45PvAI4AVgFvichMVV0KXO/LekhE/g6MBe5MQU/DMAwjDSTqIrtVVScAfxWRWDP5j09UsKq+LCJ9AupxAvCQqm4FVohIJTDcp1Wq6odep4eAE0TkfeBw4Kc+z3TgSszAGIZhNBkSzeS/33/elCBPKpwnIj/HRQi4QFU3AL2A1yPyrPIygJVR8hFAd2Cjqu6IkX83ROQc4ByA3r17N8Q1GIZhGHWQqItsof98qQHruxP4Iy622R+Bm4FfNmD5MVHVKcAUgOLi4t1aY4ZhGEbDU2c0ZT/n5Upgb59fAFXVfZKtTFXXRpR7N/C0P/wE2Csia76XEUe+DugiIm19KyYyv2EYhtEECDIP5h/ALcAoYBhQ7D+TRkR6RhyeBIQ9zGYCo0WkvYj0BQqAN4G3gALvMdYO5wgwU1UVmMsub7YxwJOp6GQYhmGkhyALjn2pqs8kW7CIPAgcCnxTRFYBVwCHikgRrovsI+BcAFVdIiIPA0uBHcB4Vd3pyzkPeA439+ZeVV3iq5gEPCQiVwPv4AyhYRiG0UQQ1xhIkEHkOtzL/XFqL5n8dnpVSw/FxcW6YIGtQGAYhpEMIrJQVYuTOSdIC2aE/4wsWHFuwoZhGIYRkyBLJh+WCUVaGqGtIcqWlFGxroKC7gWUDiglr31eY6tlGIaRMYJ4kV0eS66qVzW8Oi2D+R/Pp2RGCdVaTdX2KnKzc5n43ERmnT6LUb1HNbZ6hmEYGSGIF1lVxLYTOBrok0admjWhrSFKZpQQ2haiansVAFXbqwhtc/LN2zY3soaGYRiZIUgX2c2RxyJyE86ry4hB2ZIyqrU6Zlq1VlO2uIyxQ8ZmWCvDMIzMk8qKlh1xExuNGFSsq6hpuURTtb2KyvWVGdbIMAyjcQgyBvMezmsMnLtyD8DGX+JQ0L2A3OzcmEYmNzuXft36pV0HczAwDKMpEGQezN4RhzuAtRFBJpsd6Z4HE9oaotctvQhtC+2Wltcuj9UXrKZTu05pqz+Wg0GWZJmDgWEY9SKVeTB1dpGp6v8itk+as3HJBHnt85h1+izy2uWRm50LuJZLXjsnT6dxMQcDwzCaEkEmWhpJMqr3KFZfsJqyxWVUrq+kX7d+lB5YmlbjAuZgYBhG08IMTJro1K5Txl/m5mBgGEZTImEXmYi0EZG5mVLGqB9hB4NYZMrBwDAMI0xCA+MjGleLyDcypI9RD0oHlJIlsb/SLMmi9MDSDGtkGEZrJkgX2WbgPRGZjZvND4Cq/jZtWhkpEXYwiOdFlu4xIMMwjEiCGJjH/WY0AxrLwcAwDCOaIKFipotIB6C3qi7LgE5GPWkMBwPDMIxo6pwHIyLHAeXAs/64SERmplsxwzAMo3kTJBbZlcBwYCOAqpYD+6RRJ8MwDKMFEMTAbFfVL6NksWfzGYZhGIYniIFZIiI/BdqISIGI/AV4ta6TROReEflMRBZHyLqJyGwRqfCfXb1cROR2EakUkXdFZEjEOWN8/goRGRMhHyoi7/lzbhcRSerKDcMwjLQSxMD8BhgAbAUeBDYBEwKcNw04Kko2GZijqgXAHH8MbhGzAr+dA9wJziABVwAjcN10V4SNks9zdsR50XUZhmEYjUiQYJdbVPX3wA+Aw1T196r6dYDzXgbWR4lPAKb7/enAiRHy+9TxOtBFRHoCPwJmq+p6Vd0AzAaO8mmdVfV1deGg74soyzAMw2gCBPEiG+bXhHkXN+FykYgMTbG+PVR1jd//FNjD7/cCVkbkW+VlieSrYsjjXcM5IrJARBZ8/vnnKapuGIZhJEOQLrJ/AL9W1T6q2gcYD0ytb8W+5ZF4MZoGQlWnqGqxqhb36NEjE1UahmG0eoLM5N+pqvPCB6o6X0RSXRNmrYj0VNU1vpvrMy//BNgrIl++l30CHBolf9HL82PkbzxCISgrg4oKKCiA0lLIs1UkDcNovQRpwbwkIneJyKEi8n0R+RvwoogMifT2CshMIOwJNgZ4MkL+c+9NNhL40nelPQccKSJd/eD+kcBzPm2TiIz03mM/jygr88yfD716wYQJcMMN7rNXLyc3DMNopQRpwRT6zyui5INxXVyHxzpJRB7EtT6+KSKr/PnXAQ+LyFjgf8CpPvssoASoBLYAvwBQ1fUi8kfgLZ/vKlUNOw78Guep1gF4xm+ZJxSCkhL3GabKxwQtKYHVq6FTy4gDZo00wzCSQdxQSOuhuLhYFyxY0HAF3nOPa7FUxVjoKzcXbrsNxjb/uGDz5zt7WV3tLjU3F7KyYNYsGDWqsbUzDCPdiMhCVS1O5pwgXWRGIioqYhsXcPLK5r+KZGQjLXypVVW75Js3N65+hmE0TczA1JeCAvd3Pha5udCv+a8iWVbmWi6xqK526YZhGNGYgakvpaWurygWWVkuvZnTChpphmGkgSATLTuKyP+JyN3+uEBEjk2/as2EvDw3EJGXt6slk5u7S94CBvhbQSPNMIw0UOcgv4iUAQuBn6vqgSLSEXhVVYsyoWBD0+CD/GE2b3Z9RZWV7o1bWgqqLcLtKhRyXteRjnJh8vJalKOcYRhxSGWQP4ib8r6qWioip4GLTWaRi2PQqVNtb7FYblcTJzZLt6twYyyeF5kZF8MwYhHEwGzzSyYrgIjsi4usbMSjBc6NGTXKqR3dSGtml2EYRgYJYmCuwC2XvJeIzAAOBs5Mp1LNniBuV81wbkx0I80wDCMRdRoYVZ0tIm8DIwEBfqeqX6Rds+aMuV0ZhmEE8iI7Cdihqv9W1aeBHSJia68kwtyuDMMwAs2DuUJVvwwfqOpGdo9LZkTSCubGGIZh1EUQAxMrT5Cxm9ZLK5gbYxiGURdBDMUCEbkFuMMfj8fNizESYW5XhmG0coIYmN8A/weEI07NxhkZoy7M7cowjFZMEC+yKmByBnQxmiC2BoxhGKkS18CIyK2qOkFEnsJPsoxEVY9Pq2ZGo9OCghEYhtEIJGrB3O8/b8qEIkbTogUGIzAMI8PENTCqulBE2gDnqOrpGdTJaAI0ZjAC65YzjJZBwjEYVd0pInuLSDtV3ZYppZodLfCN2FjBCKxbzjAalsZ8PQXxIvsQeEVEZgI1rxxVvSVtWjUnmuMbMcATFw5GEMvIpCsYgXXLGUbD0tivpyATLZcDT/u8eRFbyojIRyLynoiUi8gCL+smIrNFpMJ/dvVyEZHbRaRSRN4VkSER5Yzx+StEZEx9dEqJ5rhY/fz5bnGXCRPghhvcZ69eTh5BYwQjsKWZDaPhaAqvpyBuyn8AEJHO7lBjLDuVEodFBc2cDMxR1etEZLI/ngQcDRT4bQRwJzBCRLrhQtYU47zcForITFXd0ED61U0qAxWN2V5NoonQGGvAWIxQw2g4mkJQ9zoNjIgUA1PxrRYR+RL4pao29Gz+E4BD/f504EWcgTkBuE/d0puvi0gXEenp885W1fVer9nAUcCDDaxXfJJ9IzZ2ezXJJy7TwQgao1vOMFoqTeEPW5AxmHuBX6vqPAARGYUzOIPqUa8Cz4uIAnep6hRgD1Vd49M/Bfbw+72AlRHnrvKyePLdEJFzgHMAevfuXQ+1o0jmjdgUBhhSeOIyGYygtNTZ21hYjFDDSI6m8IctyBjMzrBxAVDV+cCOetY7SlWH4Lq/xovI9yITfWtlt8mdqaKqU1S1WFWLe/To0VDFujeexlFz61b46qtdBqUpDDA08WUELEaoYTQcTSGoexAD85KI3CUih4rI90Xkb8CLIjIkcsA9GVT1E//5GfAEMBxY67u+8J+f+eyfAHtFnJ7vZfHkmSWegdmxAyZP3jWA3hTaq03hiauDcLfcbbe523fbbe64qTrkGUZTpSn8YQvSRVboP6PXgBmMa2UcnkyFIpILZKlqyO8fCVwFzATGANf5zyf9KTOB80TkIdwg/5equkZEngP+FPY28+Vckowu9aasLP4LG3YZlKOPhmuvbfz2amOM3KeAxQg1jIahsYO6B/EiO6yB69wDeEJEwvX/U1WfFZG3gIdFZCzwP+BUn38WUAJUAluAX3i91ovIH4G3fL6rwgP+GSNRqySSzZvhww+bRuuhsZ84wzAySmP+YRON18XTQikuLtYFCxY0TGH33OPmkQQxMtnZ8PTTcMopsVsP1gdkGEYTRkQWqmpxMufYypSpEgrB11/DtoARdERg5crYrQdVZ6xaUKgZwzCMIPNg2qvq1rpkrYrI+Szbtwc7Z9s2Z1Si26uNPTfGMAwjTQTxInstoKx1ECv+QhDatYPyctdSCbsuN4VYDoZhGGkiroERkW+LyFCgg4gMDrsli8ihQMeMadjUSDSfJRHbtsGzz9aO/dUU5sYYhmGkiURdZD8CzsTNL7kZEC/fBFyaXrWaMEE9x+IROXt/7NjGnxtjGIaRJhItODYdmC4iP1bVxzKoU9MmUfyFZNi5E9avT25uTAtcdyYerehSDaPFEsSLbKiIzFHVjQB+YuMFqnpZelVroiQKmJUMW7a4cDJB58a0ImeAZC7VDJFhNF3qnAcjIu+o6uAo2ds+llizo0HmwcyfD0ce6WKN1Yf27eFXv4I77nBuzNu2xZ4bEwq5cZtQjJUS8vJa1EpcyVxqLENUn2lFZqwMIz6pzIMJ4kXWRkTaR1TSAWifIH/LZ8uW+hsXcC2Yv/zFuTqLOE+z887bPfhWK3IGCHqpoZCLwNNQDngB12EzDCMJghiYGcAcERnrw7jMxq3X0joJheCEExquvJ073efWra4F87e/7Z6nKQTKzBBBL/Xqq+MbkWRtrnmLG0Z6qNPAqOr1wDXAAX77o6rekG7FmixlZbuMQjoIvx1DITdnZtIk+PRT6BjHM7wJhNlvSIKsKBAKwa23xi8jWZvbihqIhpFRAoWKUdVngGfSrEvzoKIi+Oz9VKiqgrlz4fzzdw0udOzouuVi0UTC7DcUQRYde+gh16MYj3btkrO5raiBaBgZJUiomBC7Fv9qB2QDVaraOZ2KNVkKCtwbLGgMsmTp2BEefdR1mYWJNC5hY9MEw+w3BEFWFKioqH17olHdZXODDNw3hZX/DKMlEiRcf83PUVyM/ROAkelUqklTWgrnnpu+8hNFCcjJgZ/8BHr2TD7MfjNykaprRYG6piJNnOjyBnV3tqWaDSM9pBSuP5brcnOh3m7KoRB0TmPj7cQT4V//ip8+cSLcfHNyZTa0P28jEwrBnnvGHnzv1AnWrHGtmGQ8u1vYLTKMBict4fpF5OSIwyygGPg6Sd1aDuke8X355cTp69bFlsdroUS6SIWJDFezerV7G6e5ddOQDahFi2L7WXTsCM884wzHPffUPXAfGdTa1mEzjIYnyCD/cRH7O4CPcN1krZOKivSWv76ORTlXr3aeZZFv6UR9QR98kPhNe/XVzjU6jRECGjIIQdhexpqGlJUFRUVuP5WBe1uq2TAaliBjML/IhCLNhjVrGrf+//wHZs92f9cnTnQOAaecEr+FctZZid+0N91UuzkQ3bqp51/4IA2oZKpI5FIcboiNHWsD94bRFEgUrv8vInJ7vC2TSjYp7r8ffgp8AVRHbF/g5OkmbAy2bNk16XPHjth5v/oKXnnFeb3VVV40DTQBpKHnmARtmZSWBg/zZhhGekjUggmPhB8M9AfCr4KfAEvTqVSTZjrwM78fORejG/AAbpGDMRnUZ/v2+EZixw54883Uym2gCSANPcckaMukLnfnoKtUNyPnO8Noeqhqwg14HWgbcZwNvF7XeZnagKOAZUAlMLmu/EOHDtXUuV+1ug6VqlH9Kao08y03V/Wee+pxrxx33+2KaqgqNm1SzcuLXV5enmooVDt/KOTqmDzZfYZCqvPmubxhvXJz3fG8ebXPDZrPMFoDwAJN8v0cJJryMuAgVV3vj7t6A7NfmmxeYESkDfBf4AhgFfAWcJqqxm1h1c9NuRvohtotl2gUWAf0SLGKpkIDRWlORyDo+rgUB9WnFQWwNoxApCua8nXAOyIyTUSmA28Df0pFwTQwHKhU1Q9VdRvwEGn1cKvDuIBL754+DTJC27YNFiEg3FWVl7crxlhu7i55KlWEXYpvuw0mT3af0QGo4xF0TMjikxlG/QniRTZVRJ4BRuD+n09S1U/TrlkwegErI45X4fSshYicA5wD0Lt378xolinat08cNyUVxo1r0NmF6ZhjkqpLcdAxIYtPZhj1J1CwS1xL4RC/r8BT6VEnPajqFGAKuC6yRlan4Rg/Hu67r2ENTE7OrskkDUhTmWMS1EnA3JwNo/7U2UUmItcBv8N5ji0FfisiTaWL7BNgr4jjfC9LE113hf2MR3gMpr60b++6quKRmwvXXbd7/1NOTu3P6DIB2rSJX252dov24Q3qvmxuzoZRf4K0YEqAIlWtBvDjMO8Al6ZTsYC8BRSISF+cYRlNWmej3A7ys7qz/S6JItu2de7EIs4ZqmNHZwBmzXLpsZZm7tgRnn3WNQti9T8dcwz8+9/uOD/fnbNqVe20uXPdJM02bVp0dOZogkRrTiafYRjxCeJF9i5waIQXWTfgRVUdlAH96kRESoBbgTbAvap6TaL89Q52yc+AB1xLJXLAP3wb72fXPJisLOjWzaerO+7VC/r3h6FD4fPPdzcI0QMUmzfD9OkuHVzeMWMa5g23eXOrDb4V9NJb8S0yjFqk4kUWxMCchvMkm4t7pX4PN9+kWfrR1N/AgJtR+TsgMm5YN+A24Ix6lm0YhtH0aPBoyn79l/m49V+GeXFT8iJrJM7ADIlhGEZiEhoYVVURmaWqA4GZGdLJMAzDaAEEmWj5tn2JAHEAAA3qSURBVIgMqzubYRiGYewiiBfZCOAMEfkIqMKNw2hTGeQ3DMMwmiZBDMyP0q6FYRiG0eKIa2BEJAcYB/QD3gP+oapxFh4xDMMwjNokGoOZDhTjjMvRwM0Z0cgwDMNoESTqIuvvvccQkX8AKa5cZRiGYbRGErVgtod3rGvMMAzDSJa4M/lFZCfOawyc51gHYAu7vMg6Z0TDBkZEPgf+14BFfhP4ogHLayiaol5NUScwvZLF9EqOpqhXKjrtrapJLaVYZ6gYIzEisiDZ8AmZoCnq1RR1AtMrWUyv5GiKemVKpyATLQ3DMAwjaczAGIZhGGnBDEz9mdLYCsShKerVFHUC0ytZTK/kaIp6ZUQnG4MxDMMw0oK1YAzDMIy0YAbGMAzDSA+qalsKG3AUsAyoxK3wmY469sKtJLoUWAL8zsuvBD4Byv1WEnHOJV6nZcCP6tIX6Au84eVlQLuAun2ECyNUDizwsm7AbKDCf3b1cgFu93W8CwyJKGeMz18BjImQD/XlV/pzpQ599ou4H+XAJmBCY9wr4F7gM2BxhCzt9yZeHXXodSPwga/7CaCLl/cBvoq4b39Ptf5E15hAr7R/b0B7f1zp0/sE0KssQqePgPJM3i/ivxMa/fmK+VtIx4uxpW9AG2A5sA/QDliEC63T0PX0DD8QQB7wX6C///FdGCN/f69Le/+jWu51jasv8DAw2u//HfhVQN0+Ar4ZJbsh/MMGJgPX+/0S4Bn/sI8E3oh4YD/0n139fviH8abPK/7co5P8fj4F9m6Me4VbVnwItV9Mab838eqoQ68jgbZ+//oIvfpE5osqJ6n6411jHXql/XsDfo03BMBooKwuvaLSbwYuz+T9Iv47odGfr5jXnurLrzVvwEHAcxHHlwCXZKDeJ4EjEvz4aukBPOd1jamvf4C+YNcLpla+OnT5iN0NzDKgp9/vCSzz+3cBp0XnA04D7oqQ3+VlPYEPIuS18gXQ7UjgFb/fKPeKqBdOJu5NvDoS6RWVdhIwI1G+VOqPd4113K+0f2/hc/1+W59PEukVIRdgJVDQGPcrxjuhSTxf0ZuNwaRGL9zDFWaVl6UNEekDDMY15QHOE5F3ReReEelah17x5N2Bjbor1lwy16HA8yKyUETO8bI9VHWN3/8U2CNFvXr5/Wh5UEYDD0YcN/a9gszcm3h1BOWXuH+sYfqKyDsi8pKIHBKhb7L1p/p7Sff3VnOOT//S5w/CIcBaVa2IkGX0fkW9E5rk82UGphkgIp2Ax4AJqroJuBPYFygC1tA4SymMUtUhuKUcxovI9yIT1f3N0UwrJSLtgOOBR7yoKdyrWmTi3iRbh4j8HtgBzPCiNUBvVR0MTAT+KSKB4w82wDU2ue8titOo/Scmo/crxjsh5bJSIWgdZmBS4xPcYFuYfC9rcEQkG/cgzVDVxwFUda2q7lT9//bOPdjqqorjn28oj5RHIuPAjAaY5uhY1IhZmV2LcTKxCFF8paSj2QyiNlhONAY2NaT5GKNpJjWZ0saxdIiwBB+XNB0DQXmIQajopGRmSKKCCqs/1vpxf/d4zj3nHjkHa9Zn5szdv733b++19+939/rt19q2A7gBOLKOXLX8XwaGSNqjwr8uZvZ8/P0nPjl8JPCipOEh93B8grQZuZ4Pd6V/IxwPLDezF0O+3V5XQTvqplYePSJpCjAeOCMaDsxsm5m9HO5l+PzGwU3m3+v/lzY9t533RPjgiN8jEXciPuFfyNu2+qrWJjSRVlver1QwzbEUOEjSqPhiPhWYv6szkSTgJuBJM7um5D+8FO0rwOpwzwdOldRP0ijgIHzCrqq80Zh0ApPi/rPxMd16cu0laWDhxuc8Vkf+Z1dJaz5wlpyjgM3R1V4IHCfpAzEEchw+Pr4R+I+ko6IOzmpErqDbl+XurqsS7aibWnnURNIXgG8BXzKz10v+wyT1CffoqJ+nm8y/Vhl7kqsdz60s7yTg/kLB1mEcPk+xcyipXfVVq01oIq22vF8tnZT+f/7hqzPW4V8qM1qUx9F4N3QlpeWawK/wZYQr46EPL90zI2RaS2nlVS158VU3S/Alib8B+jUg12h8lc4KfKnkjPAfCtyHL2O8F9gn/AX8NPJeBRxRSuucyHs98LWS/xF4o/IUMIc6y5Tjnr3wL9DBJb+21xWu4DbiZyr9HTi3HXVTK486cq3Hx+K7La8FTopn+ziwHDix2fx7KmMPcrX8uQH943p9hI+uJ1f4zwUuqIjblvqidpuw29+var80FZMkSZK0hBwiS5IkSVpCKpgkSZKkJaSCSZIkSVpCKpgkSZKkJaSCSZIkSVpCKpjkHUgySVeXrqdLmrmL0p4raVL9mO86n5MlPSmps8J/hKTfhnuMpC+2WpZS3h2SNkt6PGT73i5Kd4qkObsirQbz2yBp33APkJtGKfaAHCbpfklrJT0laZakXd7OSJog6dDS9RWSxoX7Yknvr3N/X0kPqGsDZtICUsEk1dgGTCwakfcKvWwMzgXOM7Njy55m9oKZFQpuDL6HoJ08aGZj8L0GZ0r6eKM3Fo34e4xzgDvNbLukAfieldlm9mHgcHwH/kUtyHcCbkUYADO73MzujcuLgR4VjJm9ie/pmNwC2ZIgFUxSjbfxM7svqQyo7IFI2hJ/O+JL9neSnpY0W9IZkpZIWiXpwFIy4yQ9KmmdpPFxfx9JV0laKjdw+PVSug9Kmo+fgVEpz2mR/mpJPwq/y/ENaTdJuqoi/siI2xe4ApgcPYrJcgsFvwiZH5P05bhniqR5ku6Jr/epkr4ZcR6RtE/EmyZpTch/W08VbGavAcuAD1X2QCQtkNRR1K+kqyWtAD4paaykhyWtCDkHxm0jJN0t6W+Sriyl9bOo6yckzSr5zy7J+uPwGybpjngGSyV9OvyHSloUadyIb94rOIOuHd2n41asF0UZXwemApdGOjMlTS/JsFpusJGo32WRx/mlOFsk/SDK+4ik/SR9Crc3d1U8uwOL91LSNGAE0CmpU9I5kq4rpXeepGvjcl7In7SKVuxAz9//9g/YAgzCTfIPBqYDMyNsLjCpHDf+dgCv4Ga8++H2i2ZF2EXAdaX778Y/bg7Cd0j3B84Hvhtx+gGP4ud9dACvAaOqyDkCeA4Yhptbvx+YEGGLqdgxHv4jCbPqwBRgTinsh8CZ4R6C7wrfK+Ktx8/fGIZb3b0g4l2LGxwEeIGuXeJDquTdASwI99Co38OqyLEA6Ai3AaeEuy9+bsfYuB4U5Z4S/oOjLp8F9o84xY7uPlEnH4m819K1Q7s4ZOzXuBFTgANwcyTgh04V556cEDLtG/L8oyT3NcQBWBXl3hT1OZOSCX58t/jICjkHhP/QUvlPDPeVdL0jc+n+Hu68pnSUBLA3viN9z7h+GDi8VCcv7e7/t//nX/ZgkqqYW2j9JTCtF7ctNbONZrYN/6deFP6r8Ia94HYz22Fu6vxp4BDcFtJZkh7HzY8PxRUQwBIze6ZKfmOBxWb2krm59VvxQ6Ka5TjgspBhMd5YHxBhnWb2qpm9hCuY31cp20rgVkln4r3AanxG0mN43cw2syfqyLQdN2wIfmrnRjNbCv6MrMsM/X1mttnMtuI9vQ+G/ymSlgOP4crs0JB/K97DmwgUNsjGAXOi/POBQXKrvccAt0Sed+EKA1zJvFJH/kaZFr20R3AjjMWzfxNXuOA9vpG9SdTMtuAfHuMlHYIrmlURth14s9QLTHYxOcGV9MR1uF2lm0t+bxNDq/LJ276lsG0l947S9Q66v2uV9okMH3a50MwWlgNiqOi15sTvNQJOMrO1FTJ8gsbKdgLeGJ8IzJB0eEkBFDxoZuMr/HbWadC/5N4aDWE9yvJtB/aQG4Ocjvd4NkmaC/Q3s7clHQl8HjfyOBX4XMhwVCipnUjlEbFuvFEh6xoqFLzc8OPLZvaKpKrljGc8Dj/863VJi0vpvmXR3SjK1VMl1OBG4Dv40dA3V4T1w5Vt0gKyB5PUxMz+jR83e27JewN+Zjf4OPieTSR9sqT3xbzMaHy4ZiHwDbkpciQdLLfU3BNLgM9K2lc+AX4a8KdeyPEqPuxVsBC4UNGiSvpYowmFst3fzDqBb+PDVXs3ePsGYEzUyf50maavZC0wXNLYyHOgel74MAhXzpsl7YcfZVCcJTLYzP6Az7N9NOIvAi4slWlMOB/A51eQdDx+xC5mtgnoI6lQBrcCR6trNdcAfHitWC23AT+CGPnihlHhPxjYFMrlEPy43npUPruaYWb2F7xXdDrdLW0PBf5lZm81kF/SBKlgknpcjQ+FFNyAN+or8ONnm+ldPIcrhz/icxlb8a/MNcBySavxI1x7/Fo1Ny1+GW6OfQWwzMx6Y0K/Ezi0mOQHvo8rzJWSnojrRukD3CJpFT4cdb2ZNTp89BDwDF7+6/Fe4zswX/k0GfhJ1P89dO9BVMZfEbL8FZ9feSiCBgILJK0E/owfkAU+HHpETPyvAS4I/1nAMVEnE/HnV7AIX1CBmb2Bf3TMkLQOP4L4ITMrDjG7A9gn0pmKz3GBz8ntIelJYDY+TFaP24BLY6HFgRVhPwfuVvcl6reHLJtKfscCdzWQV9IkaU05SZKmiZ7IJWb21SphE/CJ/2PN7Nm2C9ddlgXAtWZ2X8nvTuAyM1tX+87k3ZA9mCRJmsbMluNLgt+xR8fM5pnZ6N2pXCQNid7UGxXKpS8wL5VLa8keTJIkSdISsgeTJEmStIRUMEmSJElLSAWTJEmStIRUMEmSJElLSAWTJEmStIT/AhGD+7F6wY3OAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0, 1], s = 50, c = 'red', label = 'Customer Type 1')\n",
    "plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1, 1], s = 50, c = 'blue', label = 'Customer Type 2')\n",
    "plt.scatter(X[y_kmeans == 2, 0], X[y_kmeans == 2, 1], s = 50, c = 'green', label = 'Customer Type 3')\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 100, c = 'yellow', label = 'Centroids')\n",
    "plt.title('Type Of Customers(customer type clusters)')\n",
    "plt.xlabel('Number of items Purchased(Quantity)')\n",
    "plt.ylabel('Product price per unit in sterling(Unit Price)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-0.04883394, 14.02583104,  5.73512729]),\n",
       " array([-0.03575527,  2.87739153, 23.4184844 ]))"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Product price per unit in sterling(Unit Price)')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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I3Ex6GPROSZcDV0TEpZK+DdwWEec2i6unpyfmz+/b3GRmZs1IWhARPa3uV2aATCLivoj4fUTcWiaDyfssi4hb8vLTwF1As38HjwAujYjnI+JPwFJShrMfsDQi7o2IF4BLgSMkCXgD8KO8/8WkTMzMzNpEv5lMbdTlPq8HJP1E0q5lTpJLQvsAN+akEyTdLulCSdvmtIlAcVy0B3Nao/TtgeURsapPupmZtYkyJZmzgOmkG/iOwEnAf5NKFA3bZGry6AA/Bj4WEU8B5wK7AXsDy6g/RtqgknS8pPmS5j/66KNVn87MzLIymcxbIuK8iHg6Ip6KiPOBqRFxGbBtsx1zz7QfA5dExBUAEfFwRKyOiBeB75CqwwB6gZ0Ku++Y0xqlPwZ0S9q4T/p6IuL8iOiJiJ5x48aV+MpmZjYYymQyz0l6m6SN8uttwF/zuoa9BnKbyQXAXRFxZiF9fGGztwJ35OXZwNGSNpW0CzAZuInU0D859yTbBDgamB2px8I84Ki8/zHAlSW+j5mZDZEyY5e9C/gG8P9ImcoNwLsldQEnNNnvAOBfgUWSatMCfAp4h6S987HuA/4NICIW595idwKrgA9HxGoASScAc4AxwIURsTgf75PApZI+DywkZWpmZtYmSnVh7iTuwmxm1rqBdmHutyQjaRzpwcxJxe0jwk/7m5lZU2Wqy64Efkt6OHJ1P9uamZmtUSaT2TwiPll5JGZm1nHK9C67qji+mJmZWVllMpmPkjKaFflp/6clPVV1YGZmNvL1W10WEVsNRSBmZtZ5mk2/vHtE3C1p33rra4NfmpmZNdKsJPPvpK7L9cYWC9IIyGZmZg01m375/fn9oKELx8zMOkmz6rIjm+1YG/DSzMyskWbVZYc3WReAMxkzM2uqWXXZcZI2Ao6KiMuHMCYzM+sQTZ+TyXO+/McQxWJmZh2mzMOYv5J0kqSdJG1Xe1UemZmZjXhlxi57e37/cCEtgF0HPxwzM+skZZ7432UoAjEzs87Tb3WZpM0lfUbS+fnzZElvrj40MzMb6cq0yXwXeAH4+/y5F/h8ZRGZmVnHKJPJ7BYRXwFWAkTEc4AqjcrMzDpCmUzmBUldpMZ+JO0GPF9pVGZm1hHK9C47FfgFsJOkS4ADgOOqDMrMzDpDvyWZiPglcCRwLPADoCci5vW3X36uZp6kOyUtlvTRnL6dpLmS7snv2+Z0STpb0lJJtxenGJB0TN7+HknHFNJfKWlR3udsSa7GMzNrI2V6l10TEY9FxNURcVVE/EXSNSWOvQr494jYA9gf+LCkPYAZwDURMRm4Jn8GOBSYnF/HA+fm828HnAK8GtgPOKWWMeVt3l/Y75AyX3okm7WwlwPOuJZdZlzNAWdcy6yFvcMdkplZQw0zGUmb5Rv8SyRtW3jafxIwsb8DR8Sy2sRmEfE0cFfe7wjg4rzZxcC0vHwE8L1IbgC6JY0HpgJzI+LxiHgCmAscktdtHRE3REQA3yscqyPNWtjLyVcsonf5CgLoXb6Ck69Y5IzGzNpWs5LMvwELgN3ze+11JXBOKyfJGdM+wI3ADhGxLK/6M7BDXp4IPFDY7cGc1iz9wTrp9c5/vKT5kuY/+uijrYTeVmbOWcKKlavXSVuxcjUz5ywZpojMzJprmMlExDfy0/4nRcSuEbFLfr0iIkpnMpK2BH4MfCwinupzjiD3WqtSRJwfET0R0TNu3LiqT1eZh5avaCndzGy4lenC/GdJWwHkJ/+vKDbKNyNpLCmDuaQwydnDuaqL/P5ITu8FdirsvmNOa5a+Y530jjWhu6uldDOz4VYmk/m/EfG0pNcCbwQuIDfKN5N7el0A3BURZxZWzQZqPcSOIVW/1dLfk3uZ7Q88mavV5gAH53ahbYGDgTl53VOS9s/nek/hWB1p+tQpdI0ds05a19gxTJ86ZZgiMjNrrsxzMrVGgH8Czo+IqyWVGVbmAOBfgUWSbs1pnwLOAC6X9D7gfuBted3PgMOApcBz5GdxIuJxSZ8Dbs7bnR4Rj+flDwEXAV3Az/OrY03bJzU5zZyzhIeWr2BCdxfTp05Zk25m1m6UmkWabCBdRaqGehOwL7ACuCkiXlF9eIOvp6cn5s+fP9xhmJmNKJIWRERPq/uVqS57G6nKampELAe2A6a3eiIzMxt9yswn8xxwReHzMmBZ4z3MzMySMiUZMzOzAXEmY2ZmlXEmY2ZmlSkzQOaRefTjJyU9JelpSU/1t5+ZmVmZ52S+AhweEXdVHYyZmXWWMtVlDzuDMTOzgShTkpkv6TJgFoVplwtjkZmZmdVVJpPZmjTMy8GFtKDw7IyZmVk9ZR7GPG4oAjEzs87TMJOR9B8R8RVJ36TOnC8RcWKlkZmZ2YjXrCRTa+z3aJJmZjYgDTOZiPhpfr946MIxM7NO4if+zcysMs5kzMysMmWGlTmgTJqZmVlfZUoy3yyZZmZmto5mXZhfA/w9ME7SJwqrtgbGVB2YmZmNfM26MG8CbJm32aqQ/hRwVJVBmZlZZ2jWhfnXwK8lXRQR9w9hTGZm1iEatslIOisvniNpdt9XfweWdKGkRyTdUUg7VVKvpFvz67DCupMlLZW0RNLUQvohOW2ppBmF9F0k3ZjTL5O0Scvf3szMKtWsuuy/8vtXB3jsi4BzgO/1Sf96RKxzTEl7AEcDewITgF9Jenle/S3gTcCDwM2SZkfEncCX87EulfRt4H3AuQOM1czMKtCsumxBfv/1QA4cEb+RNKnk5kcAl0bE88CfJC0F9svrlkbEvQCSLgWOkHQX8AbgnXmbi4FTcSZjZtZWSj0nI2mupD9IulfSnyTduwHnPEHS7bk6bducNhF4oLDNgzmtUfr2wPKIWNUnvdF3OF7SfEnzH3300Q0I3czMWlHmOZkLgDOB1wKvAnry+0CcC+wG7A0sA742wOO0JCLOj4ieiOgZN27cUJzSzMwoN2nZkxHx88E4WUQ8XFuW9B3gqvyxF9ipsOmOOY0G6Y8B3ZI2zqWZ4vZmZtYmypRk5kmaKek1kvatvQZyMknjCx/fCtR6ns0Gjpa0qaRdgMnATcDNwOTck2wTUueA2RERwDzWPq9zDHDlQGIyM7PqlCnJvDq/9xTSgtTw3pCkHwAHAi+R9CBwCnCgpL3z/vcB/wYQEYslXQ7cCawCPhwRq/NxTgDmkEYZuDAiFudTfBK4VNLngYWkaj0zM2sjSoWC0aOnpyfmz/c8bGZmrZC0ICJ6+t9yXf2WZCR9tl56RJze6snMzGx0KVNd9mxheTPgzaydmtnMzKyhfjOZiFinm7Gkr5LaSMzMzJoqU5Lpa3NSl2HLZi3sZeacJTy0fAUTuruYPnUK0/Zp+GyomdmoUaZNZhGpNxikHl7jALfHZLMW9nLyFYtYsXI1AL3LV3DyFYsAnNGY2ahXpiTz5sLyKuDhwnAuo97MOUvWZDA1K1auZuacJc5kzGzUK9Mm47lkmnho+YqW0s3MRpMyT/xbExO6u1pKNzMbTZzJbKDpU6fQNXbMOmldY8cwfeqUIYth1sJeDjjjWnaZcTUHnHEtsxZ6GDczaw9Nq8skjQF+FREHDVE8I06t3WW4epe544GZtbOmmUxErJb0oqRtIuLJoQpqpJm2z8Rhu6G744GZtbMyvcueARZJmkvh6f+IOLGyqKw0dzwws3ZWJpO5Ir+sDU3o7qK3Tobijgdm1g7KdGG+WFIXsHNELBmCmKwF06dOWadNBoa+44GZWSP99i6TdDhwK/CL/HlvSbOrDszKmbbPRL505F5M7O5CwMTuLr505F5ujzGztlCmuuxUYD/gOoCIuFXSrhXGZC0azo4HZmbNlHlOZmWdnmUvVhGMmZl1ljIlmcWS3gmMkTQZOBH4n2rDMjOzTlCmJPMRYE/geeAHwFPAx6oMyszMOkOZ3mXPAZ+W9OX0MZ6uPiwzM+sEZXqXvSrPKXM76aHM2yS9ssR+F0p6RNIdhbTtJM2VdE9+3zanS9LZkpZKul3SvoV9jsnb3yPpmEL6KyUtyvucLUmtfnkzM6tWmeqyC4APRcSkiJgEfBj4bon9LgIO6ZM2A7gmIiYD1+TPAIcCk/PreOBcSJkScArwalIPt1NqGVPe5v2F/fqey8zMhlmZTGZ1RPy29iEifkeavKypiPgN8Hif5COAi/PyxcC0Qvr3IrkB6JY0HpgKzI2IxyPiCWAucEhet3VE3BARAXyvcCwzM2sTZXqX/VrSeaRG/wDeDlxXq9KKiFtaON8OEbEsL/8Z2CEvTwQeKGz3YE5rlv5gnXQzM2sjZTKZV+T3U/qk70PKdN4wkBNHREiKgezbKknHk6rh2HnnnYfilGZmRrneZYM5l8zDksZHxLJc5fVITu8Fdipst2NO6wUO7JN+XU7fsc72dUXE+cD5AD09PUOSsZmZ2dDPjDkbqPUQOwa4spD+ntzLbH/gyVytNgc4WNK2ucH/YGBOXveUpP1zr7L3FI5VOc9EaWZWTpnqsgGR9ANSKeQlkh4kVbedAVwu6X3A/cDb8uY/Aw4DlgLPAccBRMTjkj4H3Jy3Oz0iap0JPkTqwdYF/Dy/KueZKM3MylPqnDV69PT0xPz58we8/wFnXFt3/paJ3V1cP2NAzVNta9bC3mGbVtrM2oukBRHR0+p+ZR7G3FzS/5X0nfx5sqQ3DyTITjBaZqKsldh6l68gWFtic9WgmbWiTJvMd0njlr0mf+4FPl9ZRG2u0YyTnTYT5cw5S9aZCA1gxcrVzJzjeevMrLwymcxuEfEVYCWsGcts1A7hMn3qFLrGjlknrRNnohwtJTYzq1aZTOaFPP1yAEjajVSyGZVGy0yUo6XEZmbVKtO77BTS1Ms7SboEOAA4tsqg2t1omIly+tQp6/Sig84ssZlZtco8jDlX0i3A/qRqso9GxF8qj8yGVS0Tde8yM9sQ/WYykt4KXBsRV+fP3ZKmRcSsyqMbYTqty+9oKLGZWbXKtMmcEhFP1j5ExHLWH8ds1HOXXzOz9ZXJZOptU9lIASOVu/yama2vTCYzX9KZknbLrzOBBVUHNtK4y6+Z2frKZDIfAV4ALsuv50mzY1qBu/yama2vTO+yZ1k7TbI14C6/Zmbra5jJSDorIj4m6afkBzGLIuItlUY2wrjLr5nZ+pqVZP4rv391KALpBO7ya2a2roaZTEQskDQGOD4i3jWEMZmZWYdo2vAfEauBl0raZIjiMTOzDlLmeZd7geslzQaerSVGxJmVRWXDptNGLTCz4VUmk/ljfm0EbFVtODacPLW0mQ22Ml2YTwOQtHX6GE9XHpUNi2ajFjiTMbOBKDNAZg9pdsyt8ucngfdGhJ/67zDDOWqBq+nMOlOZ6rILgQ9FxG8BJL2WlOn8XZWBtatOvhlO6O6it06GUvWoBa6mM6vOcN+zygwrs7qWwQBExO+AVRtyUkn3SVok6VZJ83PadpLmSronv2+b0yXpbElLJd0uad/CcY7J298j6ZgNiamMkTrS8qyFvRxwxrXsMuNqDjjj2obxDtfU0h5c1Kwa7XDPKpPJ/FrSeZIOlPQPkv4fcJ2kfYs3/AE4KCL2joie/HkGcE1ETAauYe1QNocCk/PreOBcSJkSacqBVwP7AafUMqaqjMSbYSu/ZMM1tbQHFzWrRjvcs8pUl70iv/edQ2Yf0nAzbxikWI4ADszLFwPXAZ/M6d+LiABuyJOmjc/bzo2IxwEkzQUOAX4wSPGsp9Wb4XAXU6H1xvzhGLVguKrpzDpdO/wDV6Z32UEVnDeAX0oK4LyIOB/YISKW5fV/BnbIyxOBBwr7PpjTGqWvR9LxpFIQO++884CDbuVm2C7tDO3wS9YfDy5qVo12+AeuTHVZFV4bEfuSqsI+LOn1xZW51LLeoJwDFRHnR0RPRPSMGzduwMep12Yh4KDd1z9mOxRTYWRMQTBc1XRmnW642lmLhmWGy4joze+PSPoJqU3lYUnjI2JZrg57JG/eC+xU2H3HnNbL2uq1Wvp1VcY9bZ+JzL//cS654X/X5IABfP+G/+Xq25dxyuF7rrkxtksJYqSUEjy4qNnga4fR4cs8J7NpRDzfX1pZkrYANoqIp/PywcDpwGzgGOCM/H5l3mU2cIKkS0mN/E/mjGgO8MVCY//BwMkDiakV8+5+tG4R64neJbPsAAAUtklEQVTnVq5THdYOxdRaLOApCMxGq+H+B65MSeb3QN9eZPXSytoB+Imk2vn/OyJ+Ielm4HJJ7wPuB96Wt/8ZcBiwFHgOOA4gIh6X9Dng5rzd6bVOAFVqVhIpNqi3UwliuH/JzGz0ajZp2d+QGtK7JO1Dan4A2BrYfKAnjIh7WdtjrZj+GPCPddKDBtM9R8SFpIdFh0yjEkpNbZ1LEGZmzUsyU4FjSW0dX2NtJvMU8Klqw2pf06dO4eOX3dqwV4JIPctqpQdnKmY2mjWbtOxi4GJJ/xwRPx7CmNpO3+ddmnV7C2g6oGQ7PDtjZjZUynRhfqWk7toHSdtK+nyFMbWVek/M96fZw5nDPcSDmdlQKpPJHBoRy2sfIuIJUkP8qFDveZd+ibrjhLXLszNmZkOlTCYzRtKmtQ+SuoBNm2zfUQbyXEsEdUsq7fLsjJnZUCmTyVwCXCPpfbl78VzS2GKjwoY+11IsqYyEp+/NzAZTv5lMRHwZ+ALwt/n1uYj4StWBtYt6wzK0qnf5CmYt7G2LIR7aWdkpCcxs5FB6DGX06Onpifnz57e0T7FH2DZdY3n6+VWsfrG169Y1dgxfOnIvoNyzM6OtF1rfAUVh7TXr5O9tNlJIWlCYmqX8fv1lMpKeZu1glZsAY4FnI2LrlqNsAwPJZCDdBE/76WKeeG7lgM89sbuL62f0PzPCaLzhHnDGtXV77vW9ZlVkvqMtQzcbiIFmMmWG+t+qcBKR5nfZv9UTjWSfmbWI79/wvxt8nN7lKzjgjGv7vZm1OgdMJyjTKWLWwl6m//A2VuZSZO/yFUz/4W3AwKdPaJcpGcw6VUtD/UcyizQawKgwa2EvlwxCBlNT5hmZ0dgLrUyniFNnL16TwdSsfDE4dfbiAZ/X3crNqtVvJiPpyMLrKElnAH8dgtjawsw5SwZvYps+Gt3MRmMvtDKdIpavqF9V2Si9jNGYoZsNpTKjMB9eWF4F3EeqMhsVqr7Z1I5fbBfo3nwsYzfSOv+1d3ovtOEaULRdpmQw61Rl2mSOG4pA2lV/oy4PxvH7tgs88dxKxo4R3V1jeXLFylHTGN3fgKLbbj62bseLbTcfO+BzttOUDGadqNlQ/9+kyRTIEXFiJRG1mYN2Hzcojf711G5m9doFVq4Otth0Y2495eBKzj0SnXL4nkz/0W2sXL3213LsGHHK4Xuus10rvcU8JYNZtZqVZGr9fA8A9gAuy5//BbizyqDayby7H63s2P/8yvSf+8cuu7Xu+oGWoDq1S26ZDGEgvcU8JYNZdfob6h9JHwReGxGr8udvA78dmvCGX5VtMj+5pZfPT9uLMRKr6zyvNEaqs1dzndolt2/G+fW37+3u32YjQJmG/21Js2HWpjbeMqeNCt0N2gEGw7MvrGaXGVc3rJOsl/HUNCqtlLnJDkVJZzDP0UrG6d5iZu2lTCZzBrBQ0jzSxI+vB06tMqh2UlUGU9Ose3R319i6D282u+k2u8nWG7WgipLOYJemWimduLeYWXspM0Dmd4FXAz8Bfgy8plaVZtVavmJl3Yc3m910m91Mp//otrqZ5mA/fDjYDzi2UjrxIKRm7aXsE//7Aa8jlWJeVV041syKlas57aeLG3YI6F2+gknbd1GvJSdgnV5ZfQ1mddJgV1m18nDqtH0m8qUj92Jid7oOE7u7OnrMN7N21291WX7C/1WkeWUATpT0moj4VKWRlSTpEOAbwBjgPyPijGEOqVL9Vd9d/8fHm65vZDCrkwa7yqrVZ1ka9RZrpZ2oU3vomQ21MiWZw4A3RcSFEXEhcAjw5mrDKkfSGOBbwKGkbtbvkLTH8EY18gx2ddJgV1kNRumk1k5UZuy4VrY1s+bKNPwDdLO2d9k2FcUyEPsBSyPiXgBJl5KGvBk1z/FsKMGgVydV8YDjhj7L0krnAXeDNhs8ZTKZL7F+77IZlUZV3kTggcLnB0mdFNYh6XjgeICdd955aCKrQHfX2A0aDLKRKm6c7faAYyvtRO4GbTZ4mlaX5fljfkeaP+YK1vYuu6zZfu0mIs6PiJ6I6Bk3btxwh7Oe7q6xTOynvWJidxenvmXPDZ4Kuq/R0rW3lc4Do3EUbLOqNM1kIk2b+bOIWBYRs/Prz0MUWxm9wE6FzzvmtBGja+wYTn3Lnlw/4w2c9fa9GbvR+n3Dxo7Rmuqmvm0T795/51KfgfV6nY2mrr2ttBO5G7TZ4Ckz/fLFwDkRcfPQhFSepI2BPwD/SMpcbgbeGRENZ7EayPTLk2ZcvSFh0jV2I/668kUmdHdx0O7jmHf3o03H3jp19uI11WLbbj6WUw7fc1CqnkZ7jyn3LjMbuIFOv1wmk7kbmEyaR+ZZ0j/EERF/N4A4B52kw4CzSF2YL4yILzTbfiCZjJnZaDfQTKZMw39bT7UcET8DfjbccZiZ2fqazSezGfAB4GXAIuCC2kjMZmZmZTRr+L8Y6CFlMIcCXxuSiMzMrGM0qy7bIyL2ApB0AXDT0IRkZmadollJZs1Tf64mMzOzgWjYu0zSalJvMkg9yrqA51jbu2zrIYlwkEl6FLh/gLu/BPjLIIYzWNo1LnBsA9GucUH7xtaucUH7xtZqXC+NiJafZu+3C7OtJWn+QLrwVa1d4wLHNhDtGhe0b2ztGhe0b2xDFVfZ+WTMzMxa5kzGzMwq40ymNecPdwANtGtc4NgGol3jgvaNrV3jgvaNbUjicpuMmZlVxiUZMzOrjDMZMzOrTkT41c8LOARYAiwFZlR0jp2AeaSpoxcDH83pp5KmMbg1vw4r7HNyjmkJMLW/eIFdgBtz+mXAJi3Edx9piKFbgfk5bTtgLnBPft82pws4O5/ndmDfwnGOydvfAxxTSH9lPv7SvK9KxjWlcG1uBZ4CPjYc1w24EHgEuKOQVvk1anSOErHNBO7O5/8J0J3TJwErCtfu2wONodn3bBJX5T87YNP8eWleP6nkNbusENd9wK3DcM0a3Sva4ndtvetYxQ2zk16kKQT+COwKbALcRhpyZ7DPM772wwe2Is2Ts0f+gzupzvZ75Fg2zX9If8yxNowXuBw4Oi9/G/hgC/HdB7ykT9pXan/QpCm5v5yXDwN+nn+59wduLPyC3pvft83LtT+Em/K2yvseOsCf1Z+Blw7HdSNNTb4v696UKr9Gjc5RIraDgY3z8pcLsU0qbtfnOC3F0Oh79hNX5T874EPkjAA4GriszDXrs/5rwGeH4Zo1ule0xe/aet+/1T/k0fYCXgPMKXw+GTh5CM57JfCmJn9w68QBzMmx1o03/7L8hbU3lXW2KxHPfayfySwBxufl8cCSvHwe8I6+2wHvAM4rpJ+X08YDdxfS19muhRgPBq7Py8Ny3ehzsxmKa9ToHP3F1mfdW4FLmm03kBgafc9+rlnlP7vavnl547zdeqXnJtdCwAPA5OG4Zn3OUbtXtM3vWvHlNpn+TST9MtU8mNMqI2kSsA+pGA9wgqTbJV0oadt+4mqUvj2wPNaOQ9fq9wjgl5IWSDo+p+0QEcvy8p+BHQYY28S83De9VUcDPyh8bofrNhTXqNE5WvFe0n+sNbtIWijp15JeV4i51RgG+vdT9c9uzT55/ZN5+7JeBzwcEfcU0ob8mvW5V7Tl75ozmTYjaUvgx8DHIuIp4FxgN2BvYBnDN+XCayNiX9K0Dx+W9Priykj/2sSwRAZI2gR4C/DDnNQu122NobhGAzmHpE8Dq4BLctIyYOeI2Af4BPDfkkqPVTgI37PtfnZ1vIN1/6EZ8mtW516xQcdrVdlzOJPpXy+poa1mx5w26CSNJf3SXBIRVwBExMMRsToiXgS+A+zXT1yN0h8DuiVt3Ce9lIjoze+PkBqJ9wMeljQ+xz6e1Eg6kNh683Lf9FYcCtwSEQ/nONviujE016jROfol6VjgzcC78k2DiHg+Ih7LywtI7R0vH2AMLf/9DNHPbs0+ef02eft+5e2PJHUCqMU8pNes3r1iAMcbkt81ZzL9uxmYLGmX/N/y0cDswT6JJAEXAHdFxJmF9PGFzd4K3JGXZwNHS9pU0i7AZFJjXd148w1kHnBU3v8YUl1umdi2kLRVbZnU9nFHjuGYOsebDbxHyf7Ak7mIPQc4WNK2uQrkYFId+TLgKUn75+vwnrKxFazzn2U7XLfC+aq+Ro3O0ZSkQ4D/AN4SEc8V0sdJGpOXdyVdo3sHGEOj79ksrqH42RXjPQq4tpbJlvBGUpvFmiqlobxmje4VAzje0Pyu9ddo49ea3hl/IP138umKzvFaUtHzdgpdN4H/InUlvD3/gMcX9vl0jmkJhd5YjeIl9b65idQt8YfApiVj25XUY+c2UpfJT+f07YFrSN0ZfwVsl9MFfCuffxHQUzjWe/P5lwLHFdJ7SDeTPwLnULILc953C9J/odsU0ob8upEyuWWkuZgeBN43FNeo0TlKxLaUVCe/Trdb4J/zz/lW4Bbg8IHG0Ox7Nomr8p8dsFn+vDSv37XMNcvpFwEf6LPtUF6zRveKtvhd6/vysDJmZlYZV5eZmVllnMmYmVllnMmYmVllnMmYmVllnMmYmVllnMnYeiSFpK8VPp8k6dRBOvZFko7qf8sNPs+/SLpL0rw+6RMk/Sgv7y3psKpjKZz7QElPSro1x3bKIB33WEnnDMaxSp7vPkkvyctdSsOo1J4R2VPStZKWSPqjpNMkDfp9RtI0SXsUPp8u6Y15+WOSNu9n/00k/UZrH9S0ijiTsXqeB46s3UjaRYs3hPcB74+Ig4qJEfFQRNQyub1JzxcMpd9GxN6k5xDeLWnfsjvWbuRt5r3AFRGxWlIX6bmWMyJiCrAX6Wn9j1Zw3mmkkYcBiIjPRsSv8sePAU0zmYh4gfS8x9sriM0KnMlYPatI839/vO+KviURSc/k9wPzf7RXSrpX0hmS3iXpJkmLJO1WOMwbJc2X9AdJb877j5E0U9LNSgMj/lvhuL+VNJs0f0bfeN6Rj3+HpC/ntM+SHli7QNLMPttPyttuApwOvD2XLN6uNLLBhTnmhZKOyPscK2mWpLn5v/gTJH0ib3ODpO3ydidKujPHf2mzCxwRzwILgJf1LYlIukrSgbXrK+lrkm4DXiPpVZL+R9JtOc6t8m4TJP1C0j2SvlI41rn5Wi+WdFoh/YxCrF/NaeMk/Tj/DG6WdEBO317SL/Mx/pP0cF/Nu1j71Pc7SaNg/zJ/x+eAE4Dp+TinSjqpEMMdSgM8kq/vgnyO4wvbPCPpC/n73iBpB0l/Txqnbmb+2e1W+72UdCIwAZgnaZ6k90o6q3C890v6ev44K8dvVRqMp9X96qwX8AywNWl4/22Ak4BT87qLgKOK2+b3A4HlpOG/NyWNdXRaXvdR4KzC/r8g/YMzmfQk9WbA8cBn8jabAvNJc4YcCDwL7FInzgnA/wLjSMO1XwtMy+uuo8+T0jl9EnlIduBY4JzCui8C787L3aQnyLfI2y0lzd0xjjRi7wfydl8nDVAI8BBrnyjvrnPuA4Gr8vL2+fruWSeOq4AD83IAb8vLm5Dm/HhV/rx1/t7H5vRt8rW8H9gpb1N76ntMviZ/l8+9hLVPcdcmK/tv0kCoADuThi2BNGlVbd6Uf8oxvSTH8+dC3GeSJ9Dq872fyNfzVApD+JOeKJ/UJ86unL594fsfnpe/wtrfkYtY9/dwzWcK01IAW5KeWh+bP/8PsFfhmjw63H9vnf5yScbqijSq6/eAE1vY7eaIWBYRz5P+sH+Z0xeRbu41l0fEi5GGSb8X2J00btJ7JN1KGrZ8e1ImBHBTRPypzvleBVwXEY9GGq79EtJEUwN1MDAjx3Ad6Ya9c143LyKejohHSZnMT+t8t9uBSyS9m1QarOd1khaSrs0ZEbG4n5hWkwZChDQL6LKIuBnSzyjWDmN/TUQ8GRF/JZX4XprT3ybpFmAhKUPbI8f/V1JJ70igNm7ZG4Fz8vefDWytNNLv64Hv53NeTco0IGU0y/uJv6wTc2ntBtKgjbWf/QukTBdSyW9SKweNiGdI/3y8WdLupMxmUV63GnihUBq0CrjRy5o5izQO03cLaavI1axKDbqbFNY9X1h+sfD5Rdb9Xes7llGQqmA+EhFziitytdGzAwu/ZQL+OSKW9Inh1ZT7bv9EuiEfDnxa0l6FTKDmtxHx5j5pa65ptllh+a/5ZtifYnyrgY2VBpE8iVTyeULSRcBmEbFK0n7AP5IGhzwBeEOOYf+cUa0hFWvH1rGiT6x30ieTVxos8rGIWC6p7vfMP+M3kiYRe07SdYXjroxc7Kh9r2YXoYH/BD5Fmmr6u33WbUrKcK0iLslYQxHxOGn62vcVku8jzf8NqV587AAO/S+SNsrtNLuSqm7mAB9UGsIcSS9XGvG5mZuAf5D0EqVG8XcAv24hjqdJVWA1c4CPKN9VJe1T9kA5w90pIuYBnyRVXW1Zcvf7gL3zNdmJtUPb97UEGC/pVfmcW6l5Z4itSRn0k5J2IE2HUJuHZJuI+Bmp3e0VeftfAh8pfKe98+JvSO0tSDqUNFUvEfEEMEZSLUO4BHit1vby6iJVtdV60d1Hms4YpQ4Pu+T0bYAncgazO2na3/70/dk1XBcRN5JKR+9k3ZG6twf+EhErS5zPBsiZjPXna6RqkZrvkG7st5Gmsx1IKeN/SRnEz0ltG38l/bd5J3CLpDtIU8E2/a810pDkM0jDud8GLIiIVobhnwfsUWv4Bz5HyjRvl7Q4fy5rDPB9SYtIVVNnR0TZqqTrgT+Rvv/ZpNLjeiL1iHo78M18/eeybkmi7/a35VjuJrW3XJ9XbQVcJel24HekSbYgVY325M4AdwIfyOmnAa/P1+RI0s+v5pekThZExArSPx6flvQH0pTG10dEbTK0HwPb5eOcQGrzgtRGt7Gku4AzSFVm/bkUmJ47X+zWZ935wC+0bvf1y3MsTxTSDgKuLnEu2wAehdnMBiyXSD4eEf9aZ900UmeAgyLi/iEPbt1YrgK+HhHXFNKuAGZExB8a72kbyiUZMxuwiLiF1F14vWd4ImJWROw6nBmMpO5cqlrRJ4PZBJjlDKZ6LsmYmVllXJIxM7PKOJMxM7PKOJMxM7PKOJMxM7PKOJMxM7PK/P8dzDYTaU2TIwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=[];y=[]\n",
    "for i in range(4339):\n",
    "    x.append(X[i][0])\n",
    "    y.append(X[i][1])\n",
    "plt.scatter(x,y)\n",
    "plt.title('Plot of training data')\n",
    "plt.xlabel('Number of items Purchased(Quantity)')\n",
    "plt.ylabel('Product price per unit in sterling(Unit Price)')"
   ]
  }
 ],
 "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}