Code_Problem_4_Sai_Thatigotla.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "from matplotlib import pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from kneed import KneeLocator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('OnlineRetail.csv',delimiter=',', encoding = \"ISO-8859-1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>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>01-12-2010 08:26</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>01-12-2010 08:26</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>01-12-2010 08:26</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>01-12-2010 08:26</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>01-12-2010 08:26</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541904</th>\n",
       "      <td>581587</td>\n",
       "      <td>22613</td>\n",
       "      <td>PACK OF 20 SPACEBOY NAPKINS</td>\n",
       "      <td>12</td>\n",
       "      <td>09-12-2011 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>09-12-2011 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>09-12-2011 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>09-12-2011 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>09-12-2011 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       01-12-2010 08:26       2.55     17850.0  United Kingdom  \n",
       "1       01-12-2010 08:26       3.39     17850.0  United Kingdom  \n",
       "2       01-12-2010 08:26       2.75     17850.0  United Kingdom  \n",
       "3       01-12-2010 08:26       3.39     17850.0  United Kingdom  \n",
       "4       01-12-2010 08:26       3.39     17850.0  United Kingdom  \n",
       "...                  ...        ...         ...             ...  \n",
       "541904  09-12-2011 12:50       0.85     12680.0          France  \n",
       "541905  09-12-2011 12:50       2.10     12680.0          France  \n",
       "541906  09-12-2011 12:50       4.15     12680.0          France  \n",
       "541907  09-12-2011 12:50       4.15     12680.0          France  \n",
       "541908  09-12-2011 12:50       4.95     12680.0          France  \n",
       "\n",
       "[541909 rows x 8 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>count</th>\n",
       "      <td>541909.000000</td>\n",
       "      <td>541909.000000</td>\n",
       "      <td>406829.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.552250</td>\n",
       "      <td>4.611114</td>\n",
       "      <td>15287.690570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>218.081158</td>\n",
       "      <td>96.759853</td>\n",
       "      <td>1713.600303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-80995.000000</td>\n",
       "      <td>-11062.060000</td>\n",
       "      <td>12346.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>13953.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.080000</td>\n",
       "      <td>15152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.130000</td>\n",
       "      <td>16791.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80995.000000</td>\n",
       "      <td>38970.000000</td>\n",
       "      <td>18287.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Quantity      UnitPrice     CustomerID\n",
       "count  541909.000000  541909.000000  406829.000000\n",
       "mean        9.552250       4.611114   15287.690570\n",
       "std       218.081158      96.759853    1713.600303\n",
       "min    -80995.000000  -11062.060000   12346.000000\n",
       "25%         1.000000       1.250000   13953.000000\n",
       "50%         3.000000       2.080000   15152.000000\n",
       "75%        10.000000       4.130000   16791.000000\n",
       "max     80995.000000   38970.000000   18287.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo      541909\n",
       "StockCode      541909\n",
       "Description    540455\n",
       "Quantity       541909\n",
       "InvoiceDate    541909\n",
       "UnitPrice      541909\n",
       "CustomerID     406829\n",
       "Country        541909\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Total Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347.0</td>\n",
       "      <td>4310.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348.0</td>\n",
       "      <td>1797.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349.0</td>\n",
       "      <td>1757.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350.0</td>\n",
       "      <td>334.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Total Amount\n",
       "0     12346.0          0.00\n",
       "1     12347.0       4310.00\n",
       "2     12348.0       1797.24\n",
       "3     12349.0       1757.55\n",
       "4     12350.0        334.40"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Total Amount']= data['Quantity'] * data['UnitPrice']\n",
    "\n",
    "total_amount = data['Total Amount'].groupby(data['CustomerID']).sum()\n",
    "total_amount = pd.DataFrame(total_amount).reset_index()\n",
    "total_amount.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>InvoiceNo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347.0</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348.0</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349.0</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350.0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  InvoiceNo\n",
       "0     12346.0          2\n",
       "1     12347.0        182\n",
       "2     12348.0         31\n",
       "3     12349.0         73\n",
       "4     12350.0         17"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchases = data['InvoiceNo'].groupby(data['CustomerID']).count()\n",
    "purchases = pd.DataFrame(purchases).reset_index()\n",
    "purchases.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Last_purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346.0</td>\n",
       "      <td>325 days 02:33:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347.0</td>\n",
       "      <td>1 days 20:58:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348.0</td>\n",
       "      <td>74 days 23:37:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349.0</td>\n",
       "      <td>18 days 02:59:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350.0</td>\n",
       "      <td>309 days 20:49:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID     Last_purchase\n",
       "0     12346.0 325 days 02:33:00\n",
       "1     12347.0   1 days 20:58:00\n",
       "2     12348.0  74 days 23:37:00\n",
       "3     12349.0  18 days 02:59:00\n",
       "4     12350.0 309 days 20:49:00"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final = data['InvoiceDate'].max()\n",
    "data['Last_purchase'] = final - data['InvoiceDate']\n",
    "LP = data.groupby(data['CustomerID']).min()['Last_purchase']\n",
    "LP = pd.DataFrame(LP).reset_index()\n",
    "LP.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "      <th>Total Amount</th>\n",
       "      <th>Last_purchase</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>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>15.30</td>\n",
       "      <td>373 days 04:24:00</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>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "      <td>373 days 04:24:00</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>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>22.00</td>\n",
       "      <td>373 days 04:24:00</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>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "      <td>373 days 04:24:00</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>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "      <td>373 days 04:24:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>2011-12-09 12:50:00</td>\n",
       "      <td>0.85</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>10.20</td>\n",
       "      <td>0 days 00:00:00</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>2011-12-09 12:50:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>12.60</td>\n",
       "      <td>0 days 00:00:00</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>2011-12-09 12:50:00</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>16.60</td>\n",
       "      <td>0 days 00:00:00</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>2011-12-09 12:50:00</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>16.60</td>\n",
       "      <td>0 days 00:00:00</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>2011-12-09 12:50:00</td>\n",
       "      <td>4.95</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>14.85</td>\n",
       "      <td>0 days 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>406829 rows × 10 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      2010-12-01 08:26:00       2.55     17850.0  United Kingdom   \n",
       "1      2010-12-01 08:26:00       3.39     17850.0  United Kingdom   \n",
       "2      2010-12-01 08:26:00       2.75     17850.0  United Kingdom   \n",
       "3      2010-12-01 08:26:00       3.39     17850.0  United Kingdom   \n",
       "4      2010-12-01 08:26:00       3.39     17850.0  United Kingdom   \n",
       "...                    ...        ...         ...             ...   \n",
       "541904 2011-12-09 12:50:00       0.85     12680.0          France   \n",
       "541905 2011-12-09 12:50:00       2.10     12680.0          France   \n",
       "541906 2011-12-09 12:50:00       4.15     12680.0          France   \n",
       "541907 2011-12-09 12:50:00       4.15     12680.0          France   \n",
       "541908 2011-12-09 12:50:00       4.95     12680.0          France   \n",
       "\n",
       "        Total Amount     Last_purchase  \n",
       "0              15.30 373 days 04:24:00  \n",
       "1              20.34 373 days 04:24:00  \n",
       "2              22.00 373 days 04:24:00  \n",
       "3              20.34 373 days 04:24:00  \n",
       "4              20.34 373 days 04:24:00  \n",
       "...              ...               ...  \n",
       "541904         10.20   0 days 00:00:00  \n",
       "541905         12.60   0 days 00:00:00  \n",
       "541906         16.60   0 days 00:00:00  \n",
       "541907         16.60   0 days 00:00:00  \n",
       "541908         14.85   0 days 00:00:00  \n",
       "\n",
       "[406829 rows x 10 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "quant = pd.merge(total_amount, purchases, how='inner', on='CustomerID')\n",
    "quant = pd.merge(quant, LP, how='inner', on='CustomerID')\n",
    "quant['Last_purchase'] = quant['Last_purchase'].dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\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>CustomerID</th>\n",
       "      <th>Total Amount</th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>Last_purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347.0</td>\n",
       "      <td>4310.00</td>\n",
       "      <td>182</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348.0</td>\n",
       "      <td>1797.24</td>\n",
       "      <td>31</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349.0</td>\n",
       "      <td>1757.55</td>\n",
       "      <td>73</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350.0</td>\n",
       "      <td>334.40</td>\n",
       "      <td>17</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4367</th>\n",
       "      <td>18280.0</td>\n",
       "      <td>180.60</td>\n",
       "      <td>10</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4368</th>\n",
       "      <td>18281.0</td>\n",
       "      <td>80.82</td>\n",
       "      <td>7</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4369</th>\n",
       "      <td>18282.0</td>\n",
       "      <td>176.60</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4370</th>\n",
       "      <td>18283.0</td>\n",
       "      <td>2094.88</td>\n",
       "      <td>756</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4371</th>\n",
       "      <td>18287.0</td>\n",
       "      <td>1837.28</td>\n",
       "      <td>70</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4372 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CustomerID  Total Amount  InvoiceNo  Last_purchase\n",
       "0        12346.0          0.00          2            325\n",
       "1        12347.0       4310.00        182              1\n",
       "2        12348.0       1797.24         31             74\n",
       "3        12349.0       1757.55         73             18\n",
       "4        12350.0        334.40         17            309\n",
       "...          ...           ...        ...            ...\n",
       "4367     18280.0        180.60         10            277\n",
       "4368     18281.0         80.82          7            180\n",
       "4369     18282.0        176.60         13              7\n",
       "4370     18283.0       2094.88        756              3\n",
       "4371     18287.0       1837.28         70             42\n",
       "\n",
       "[4372 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "quant_arr = quant[['Total Amount', 'InvoiceNo', 'Last_purchase']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cost =[] \n",
    "for i in range(1, 11): \n",
    "    KM = KMeans(n_clusters = i, max_iter = 500) \n",
    "    KM.fit(quant[['Total Amount', 'InvoiceNo']]) \n",
    "      \n",
    "    # calculates squared error \n",
    "    # for the clustered points \n",
    "    cost.append(KM.inertia_)      \n",
    "    \n",
    "# plot the cost against K values \n",
    "plt.plot(range(1, 11), cost, color ='g', linewidth ='3') \n",
    "plt.xlabel(\"Value of K\") \n",
    "plt.ylabel(\"Sqaured Error (Cost)\")\n",
    "plt.title('kmeans on amount and frequency of purchases')\n",
    "plt.show() # clear the plot \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "kn = KneeLocator(range(1, 11), cost, curve='convex', direction='decreasing')\n",
    "print(kn.knee)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cost =[] \n",
    "for i in range(1, 11): \n",
    "    KM = KMeans(n_clusters = i, max_iter = 500) \n",
    "    KM.fit(quant_arr) \n",
    "      \n",
    "    # calculates squared error \n",
    "    # for the clustered points \n",
    "    cost.append(KM.inertia_)      \n",
    "    \n",
    "# plot the cost against K values \n",
    "plt.plot(range(1, 11), cost, color ='g', linewidth ='3') \n",
    "plt.xlabel(\"Value of K\") \n",
    "plt.ylabel(\"Sqaured Error (Cost)\")\n",
    "plt.title('kmeans on all total amoount, frequency, and last purchase (days)')\n",
    "plt.show() # clear the plot \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "kn = KneeLocator(range(1, 11), cost, curve='convex', direction='decreasing')\n",
    "print(kn.knee)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans= KMeans(n_clusters=3)\n",
    "#kmeans.fit(quant[['Total Amount', 'InvoiceNo', 'Last_purchase']])\n",
    "kmeans.fit(quant[['Total Amount', 'InvoiceNo']])\n",
    "#pred = kmeans.predict(quant[['Total Amount', 'InvoiceNo', 'Last_purchase']])\n",
    "pred = kmeans.predict(quant[['Total Amount', 'InvoiceNo']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4372, 3)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quant_arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "cdict = {0: 'red', 1: 'blue', 2:'green', 3: 'purple', 4: 'yellow', 5:'black'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "group = pred\n",
    "\n",
    "ig = plt.subplots()\n",
    "ax = plt.axes()\n",
    "for g in np.unique(group):\n",
    "    ix = np.where(group == g)\n",
    "    ax.scatter(quant_arr[ix,0], quant_arr[ix,1], c = cdict[g], label = g)\n",
    "ax.legend()\n",
    "plt.title(\"clusters on kmeans on amount vs frequency of purchases\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans= KMeans(n_clusters=3)\n",
    "kmeans.fit(quant_arr)\n",
    "pred = kmeans.predict(quant_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "group = pred\n",
    "\n",
    "ig = plt.subplots()\n",
    "ax = plt.axes(projection='3d')\n",
    "for g in np.unique(group):\n",
    "    ix = np.where(group == g)\n",
    "    ax.scatter3D(quant_arr[ix,0], quant_arr[ix,1], quant_arr[ix,2], c = cdict[g], label = g)\n",
    "ax.legend()\n",
    "ax.set_xlabel('Total Amount')\n",
    "ax.set_ylabel('frequency of purchases')\n",
    "ax.set_zlabel('Last_purchase')\n",
    "plt.title(\"kmeans on all total amoount, frequency, and last purchase (days)\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}