K-Mean-Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-Mean Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Overview**<br>\n",
    "Online retail is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will be using the online reatil trasnational dataset to build a RFM clustering and choose the best set of customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing Libraries\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn.cluster import KMeans\n",
    "import seaborn as sns\n",
    "from scipy.cluster.hierarchy import linkage\n",
    "from scipy.cluster.hierarchy import dendrogram\n",
    "from scipy.cluster.hierarchy import cut_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class KMeans in module sklearn.cluster.k_means_:\n",
      "\n",
      "class KMeans(sklearn.base.BaseEstimator, sklearn.base.ClusterMixin, sklearn.base.TransformerMixin)\n",
      " |  K-Means clustering\n",
      " |  \n",
      " |  Read more in the :ref:`User Guide <k_means>`.\n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  \n",
      " |  n_clusters : int, optional, default: 8\n",
      " |      The number of clusters to form as well as the number of\n",
      " |      centroids to generate.\n",
      " |  \n",
      " |  init : {'k-means++', 'random' or an ndarray}\n",
      " |      Method for initialization, defaults to 'k-means++':\n",
      " |  \n",
      " |      'k-means++' : selects initial cluster centers for k-mean\n",
      " |      clustering in a smart way to speed up convergence. See section\n",
      " |      Notes in k_init for more details.\n",
      " |  \n",
      " |      'random': choose k observations (rows) at random from data for\n",
      " |      the initial centroids.\n",
      " |  \n",
      " |      If an ndarray is passed, it should be of shape (n_clusters, n_features)\n",
      " |      and gives the initial centers.\n",
      " |  \n",
      " |  n_init : int, default: 10\n",
      " |      Number of time the k-means algorithm will be run with different\n",
      " |      centroid seeds. The final results will be the best output of\n",
      " |      n_init consecutive runs in terms of inertia.\n",
      " |  \n",
      " |  max_iter : int, default: 300\n",
      " |      Maximum number of iterations of the k-means algorithm for a\n",
      " |      single run.\n",
      " |  \n",
      " |  tol : float, default: 1e-4\n",
      " |      Relative tolerance with regards to inertia to declare convergence\n",
      " |  \n",
      " |  precompute_distances : {'auto', True, False}\n",
      " |      Precompute distances (faster but takes more memory).\n",
      " |  \n",
      " |      'auto' : do not precompute distances if n_samples * n_clusters > 12\n",
      " |      million. This corresponds to about 100MB overhead per job using\n",
      " |      double precision.\n",
      " |  \n",
      " |      True : always precompute distances\n",
      " |  \n",
      " |      False : never precompute distances\n",
      " |  \n",
      " |  verbose : int, default 0\n",
      " |      Verbosity mode.\n",
      " |  \n",
      " |  random_state : int, RandomState instance or None, optional, default: None\n",
      " |      If int, random_state is the seed used by the random number generator;\n",
      " |      If RandomState instance, random_state is the random number generator;\n",
      " |      If None, the random number generator is the RandomState instance used\n",
      " |      by `np.random`.\n",
      " |  \n",
      " |  copy_x : boolean, default True\n",
      " |      When pre-computing distances it is more numerically accurate to center\n",
      " |      the data first.  If copy_x is True, then the original data is not\n",
      " |      modified.  If False, the original data is modified, and put back before\n",
      " |      the function returns, but small numerical differences may be introduced\n",
      " |      by subtracting and then adding the data mean.\n",
      " |  \n",
      " |  n_jobs : int\n",
      " |      The number of jobs to use for the computation. This works by computing\n",
      " |      each of the n_init runs in parallel.\n",
      " |  \n",
      " |      If -1 all CPUs are used. If 1 is given, no parallel computing code is\n",
      " |      used at all, which is useful for debugging. For n_jobs below -1,\n",
      " |      (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one\n",
      " |      are used.\n",
      " |  \n",
      " |  algorithm : \"auto\", \"full\" or \"elkan\", default=\"auto\"\n",
      " |      K-means algorithm to use. The classical EM-style algorithm is \"full\".\n",
      " |      The \"elkan\" variation is more efficient by using the triangle\n",
      " |      inequality, but currently doesn't support sparse data. \"auto\" chooses\n",
      " |      \"elkan\" for dense data and \"full\" for sparse data.\n",
      " |  \n",
      " |  Attributes\n",
      " |  ----------\n",
      " |  cluster_centers_ : array, [n_clusters, n_features]\n",
      " |      Coordinates of cluster centers\n",
      " |  \n",
      " |  labels_ :\n",
      " |      Labels of each point\n",
      " |  \n",
      " |  inertia_ : float\n",
      " |      Sum of squared distances of samples to their closest cluster center.\n",
      " |  \n",
      " |  Examples\n",
      " |  --------\n",
      " |  \n",
      " |  >>> from sklearn.cluster import KMeans\n",
      " |  >>> import numpy as np\n",
      " |  >>> X = np.array([[1, 2], [1, 4], [1, 0],\n",
      " |  ...               [4, 2], [4, 4], [4, 0]])\n",
      " |  >>> kmeans = KMeans(n_clusters=2, random_state=0).fit(X)\n",
      " |  >>> kmeans.labels_\n",
      " |  array([0, 0, 0, 1, 1, 1], dtype=int32)\n",
      " |  >>> kmeans.predict([[0, 0], [4, 4]])\n",
      " |  array([0, 1], dtype=int32)\n",
      " |  >>> kmeans.cluster_centers_\n",
      " |  array([[ 1.,  2.],\n",
      " |         [ 4.,  2.]])\n",
      " |  \n",
      " |  See also\n",
      " |  --------\n",
      " |  \n",
      " |  MiniBatchKMeans\n",
      " |      Alternative online implementation that does incremental updates\n",
      " |      of the centers positions using mini-batches.\n",
      " |      For large scale learning (say n_samples > 10k) MiniBatchKMeans is\n",
      " |      probably much faster than the default batch implementation.\n",
      " |  \n",
      " |  Notes\n",
      " |  ------\n",
      " |  The k-means problem is solved using Lloyd's algorithm.\n",
      " |  \n",
      " |  The average complexity is given by O(k n T), were n is the number of\n",
      " |  samples and T is the number of iteration.\n",
      " |  \n",
      " |  The worst case complexity is given by O(n^(k+2/p)) with\n",
      " |  n = n_samples, p = n_features. (D. Arthur and S. Vassilvitskii,\n",
      " |  'How slow is the k-means method?' SoCG2006)\n",
      " |  \n",
      " |  In practice, the k-means algorithm is very fast (one of the fastest\n",
      " |  clustering algorithms available), but it falls in local minima. That's why\n",
      " |  it can be useful to restart it several times.\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      KMeans\n",
      " |      sklearn.base.BaseEstimator\n",
      " |      sklearn.base.ClusterMixin\n",
      " |      sklearn.base.TransformerMixin\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, n_clusters=8, init='k-means++', n_init=10, max_iter=300, tol=0.0001, precompute_distances='auto', verbose=0, random_state=None, copy_x=True, n_jobs=1, algorithm='auto')\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  fit(self, X, y=None)\n",
      " |      Compute k-means clustering.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like or sparse matrix, shape=(n_samples, n_features)\n",
      " |          Training instances to cluster.\n",
      " |      \n",
      " |      y : Ignored\n",
      " |  \n",
      " |  fit_predict(self, X, y=None)\n",
      " |      Compute cluster centers and predict cluster index for each sample.\n",
      " |      \n",
      " |      Convenience method; equivalent to calling fit(X) followed by\n",
      " |      predict(X).\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : {array-like, sparse matrix}, shape = [n_samples, n_features]\n",
      " |          New data to transform.\n",
      " |      \n",
      " |      u : Ignored\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      labels : array, shape [n_samples,]\n",
      " |          Index of the cluster each sample belongs to.\n",
      " |  \n",
      " |  fit_transform(self, X, y=None)\n",
      " |      Compute clustering and transform X to cluster-distance space.\n",
      " |      \n",
      " |      Equivalent to fit(X).transform(X), but more efficiently implemented.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : {array-like, sparse matrix}, shape = [n_samples, n_features]\n",
      " |          New data to transform.\n",
      " |      \n",
      " |      y : Ignored\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      X_new : array, shape [n_samples, k]\n",
      " |          X transformed in the new space.\n",
      " |  \n",
      " |  predict(self, X)\n",
      " |      Predict the closest cluster each sample in X belongs to.\n",
      " |      \n",
      " |      In the vector quantization literature, `cluster_centers_` is called\n",
      " |      the code book and each value returned by `predict` is the index of\n",
      " |      the closest code in the code book.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : {array-like, sparse matrix}, shape = [n_samples, n_features]\n",
      " |          New data to predict.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      labels : array, shape [n_samples,]\n",
      " |          Index of the cluster each sample belongs to.\n",
      " |  \n",
      " |  score(self, X, y=None)\n",
      " |      Opposite of the value of X on the K-means objective.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : {array-like, sparse matrix}, shape = [n_samples, n_features]\n",
      " |          New data.\n",
      " |      \n",
      " |      y : Ignored\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      score : float\n",
      " |          Opposite of the value of X on the K-means objective.\n",
      " |  \n",
      " |  transform(self, X)\n",
      " |      Transform X to a cluster-distance space.\n",
      " |      \n",
      " |      In the new space, each dimension is the distance to the cluster\n",
      " |      centers.  Note that even if X is sparse, the array returned by\n",
      " |      `transform` will typically be dense.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : {array-like, sparse matrix}, shape = [n_samples, n_features]\n",
      " |          New data to transform.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      X_new : array, shape [n_samples, k]\n",
      " |          X transformed in the new space.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.BaseEstimator:\n",
      " |  \n",
      " |  __getstate__(self)\n",
      " |  \n",
      " |  __repr__(self)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __setstate__(self, state)\n",
      " |  \n",
      " |  get_params(self, deep=True)\n",
      " |      Get parameters for this estimator.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      deep : boolean, optional\n",
      " |          If True, will return the parameters for this estimator and\n",
      " |          contained subobjects that are estimators.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      params : mapping of string to any\n",
      " |          Parameter names mapped to their values.\n",
      " |  \n",
      " |  set_params(self, **params)\n",
      " |      Set the parameters of this estimator.\n",
      " |      \n",
      " |      The method works on simple estimators as well as on nested objects\n",
      " |      (such as pipelines). The latter have parameters of the form\n",
      " |      ``<component>__<parameter>`` so that it's possible to update each\n",
      " |      component of a nested object.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from sklearn.base.BaseEstimator:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(KMeans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reading Dataset\n",
    "retail = pd.read_csv(\"Online Retail.csv\",  sep = ',',encoding = \"ISO-8859-1\", header= 0)\n",
    "# parse date\n",
    "retail['InvoiceDate'] = pd.to_datetime(retail['InvoiceDate'], format = \"%d-%m-%Y %H:%M\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>2010-12-01 08:26:00</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>2010-12-01 08:26:00</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>2010-12-01 08:26:00</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>2010-12-01 08:26:00</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>2010-12-01 08:26:00</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 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  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retail.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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",
      "InvoiceNo      541909 non-null object\n",
      "StockCode      541909 non-null object\n",
      "Description    540455 non-null object\n",
      "Quantity       541909 non-null int64\n",
      "InvoiceDate    541909 non-null datetime64[ns]\n",
      "UnitPrice      541909 non-null float64\n",
      "CustomerID     406829 non-null float64\n",
      "Country        541909 non-null object\n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
      "memory usage: 33.1+ MB\n"
     ]
    }
   ],
   "source": [
    "#Sanity Check\n",
    "retail.shape\n",
    "retail.describe()\n",
    "retail.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo       0.000000\n",
       "StockCode       0.000000\n",
       "Description     0.268311\n",
       "Quantity        0.000000\n",
       "InvoiceDate     0.000000\n",
       "UnitPrice       0.000000\n",
       "CustomerID     24.926694\n",
       "Country         0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Na Handling\n",
    "retail.isnull().values.any()\n",
    "retail.isnull().values.sum()\n",
    "retail.isnull().sum()*100/retail.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping the na cells\n",
    "order_wise = retail.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo      0\n",
       "StockCode      0\n",
       "Description    0\n",
       "Quantity       0\n",
       "InvoiceDate    0\n",
       "UnitPrice      0\n",
       "CustomerID     0\n",
       "Country        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sanity check\n",
    "order_wise.shape\n",
    "order_wise.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Amount\n",
       "0   15.30\n",
       "1   20.34\n",
       "2   22.00\n",
       "3   20.34\n",
       "4   20.34"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#RFM implementation\n",
    "amount  = pd.DataFrame(order_wise.Quantity * order_wise.UnitPrice, columns = [\"Amount\"])\n",
    "amount.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "        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>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   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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merging amount in order_wise\n",
    "order_wise = pd.concat(objs = [order_wise, amount], axis = 1, ignore_index = False)\n",
    "#Monetary Function\n",
    "monetary = order_wise.groupby(\"CustomerID\").Amount.sum()\n",
    "monetary = monetary.reset_index()\n",
    "monetary.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If in the above result you get a column with name level_1, uncomment the below code and run it, else ignore it and keeping moving."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#monetary.drop(['level_1'], axis = 1, inplace = True)\n",
    "#monetary.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Frequency function\n",
    "frequency = order_wise[['CustomerID', 'InvoiceNo']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>Frequency</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  Frequency\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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = frequency.groupby(\"CustomerID\").InvoiceNo.count()\n",
    "k = pd.DataFrame(k)\n",
    "k = k.reset_index()\n",
    "k.columns = [\"CustomerID\", \"Frequency\"]\n",
    "k.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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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>CustomerID</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Frequency</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347.0</td>\n",
       "      <td>4310.00</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348.0</td>\n",
       "      <td>1797.24</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349.0</td>\n",
       "      <td>1757.55</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350.0</td>\n",
       "      <td>334.40</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID   Amount  Frequency\n",
       "0     12346.0     0.00          2\n",
       "1     12347.0  4310.00        182\n",
       "2     12348.0  1797.24         31\n",
       "3     12349.0  1757.55         73\n",
       "4     12350.0   334.40         17"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating master dataset\n",
    "master = monetary.merge(k, on = \"CustomerID\", how = \"inner\")\n",
    "master.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sumit Shukla\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>InvoiceDate</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17850.0</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>374 days 04:24:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17850.0</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>374 days 04:24:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17850.0</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>374 days 04:24:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17850.0</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>374 days 04:24:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17850.0</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>374 days 04:24:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID         InvoiceDate              diff\n",
       "0     17850.0 2010-12-01 08:26:00 374 days 04:24:00\n",
       "1     17850.0 2010-12-01 08:26:00 374 days 04:24:00\n",
       "2     17850.0 2010-12-01 08:26:00 374 days 04:24:00\n",
       "3     17850.0 2010-12-01 08:26:00 374 days 04:24:00\n",
       "4     17850.0 2010-12-01 08:26:00 374 days 04:24:00"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Generating recency function\n",
    "recency  = order_wise[['CustomerID','InvoiceDate']]\n",
    "maximum = max(recency.InvoiceDate)\n",
    "maximum = maximum + pd.DateOffset(days=1)\n",
    "recency['diff'] = maximum - recency.InvoiceDate\n",
    "recency.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = recency.groupby('CustomerID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID\n",
       "12346.0   326 days 02:33:00\n",
       "12347.0     2 days 20:58:00\n",
       "12348.0    75 days 23:37:00\n",
       "12349.0    19 days 02:59:00\n",
       "12350.0   310 days 20:49:00\n",
       "12352.0    36 days 22:13:00\n",
       "12353.0   204 days 19:03:00\n",
       "12354.0   232 days 23:39:00\n",
       "12355.0   214 days 23:01:00\n",
       "12356.0    23 days 04:10:00\n",
       "12357.0    33 days 20:43:00\n",
       "12358.0     2 days 02:24:00\n",
       "12359.0     8 days 01:29:00\n",
       "12360.0    52 days 21:28:00\n",
       "12361.0   287 days 22:59:00\n",
       "12362.0     3 days 21:10:00\n",
       "12363.0   110 days 02:32:00\n",
       "12364.0     8 days 02:28:00\n",
       "12365.0   291 days 22:46:00\n",
       "12367.0     4 days 20:02:00\n",
       "12370.0    51 days 21:59:00\n",
       "12371.0    45 days 02:34:00\n",
       "12372.0    72 days 00:38:00\n",
       "12373.0   311 days 23:40:00\n",
       "12374.0    25 days 21:13:00\n",
       "12375.0     3 days 01:23:00\n",
       "12377.0   315 days 21:05:00\n",
       "12378.0   130 days 02:16:00\n",
       "12379.0    82 days 02:41:00\n",
       "12380.0    22 days 01:23:00\n",
       "                 ...       \n",
       "18245.0     7 days 22:02:00\n",
       "18246.0    24 days 01:01:00\n",
       "18248.0   114 days 06:36:00\n",
       "18249.0    17 days 21:43:00\n",
       "18250.0   301 days 22:51:00\n",
       "18251.0    87 days 21:47:00\n",
       "18252.0    51 days 00:07:00\n",
       "18255.0    89 days 23:34:00\n",
       "18256.0   355 days 04:23:00\n",
       "18257.0    39 days 22:02:00\n",
       "18259.0    25 days 00:16:00\n",
       "18260.0   173 days 00:13:00\n",
       "18261.0    43 days 21:14:00\n",
       "18262.0   140 days 20:46:00\n",
       "18263.0    23 days 20:31:00\n",
       "18265.0    72 days 22:40:00\n",
       "18268.0   134 days 17:37:00\n",
       "18269.0   358 days 21:11:00\n",
       "18270.0    38 days 22:53:00\n",
       "18272.0     3 days 00:07:00\n",
       "18273.0     2 days 23:34:00\n",
       "18274.0    18 days 02:32:00\n",
       "18276.0    21 days 19:49:00\n",
       "18277.0    58 days 21:28:00\n",
       "18278.0    74 days 00:52:00\n",
       "18280.0   278 days 02:58:00\n",
       "18281.0   181 days 01:57:00\n",
       "18282.0     8 days 01:07:00\n",
       "18283.0     4 days 00:48:00\n",
       "18287.0    43 days 03:21:00\n",
       "Name: diff, Length: 4372, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.diff.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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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>Recency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346.0</td>\n",
       "      <td>326 days 02:33:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347.0</td>\n",
       "      <td>2 days 20:58:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348.0</td>\n",
       "      <td>75 days 23:37:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349.0</td>\n",
       "      <td>19 days 02:59:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350.0</td>\n",
       "      <td>310 days 20:49:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID           Recency\n",
       "0     12346.0 326 days 02:33:00\n",
       "1     12347.0   2 days 20:58:00\n",
       "2     12348.0  75 days 23:37:00\n",
       "3     12349.0  19 days 02:59:00\n",
       "4     12350.0 310 days 20:49:00"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dataframe merging by recency\n",
    "df = pd.DataFrame(recency.groupby('CustomerID').diff.min())\n",
    "df = df.reset_index()\n",
    "df.columns = [\"CustomerID\", \"Recency\"]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Recency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>326 days 02:33:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347.0</td>\n",
       "      <td>182</td>\n",
       "      <td>4310.00</td>\n",
       "      <td>2 days 20:58:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348.0</td>\n",
       "      <td>31</td>\n",
       "      <td>1797.24</td>\n",
       "      <td>75 days 23:37:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349.0</td>\n",
       "      <td>73</td>\n",
       "      <td>1757.55</td>\n",
       "      <td>19 days 02:59:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350.0</td>\n",
       "      <td>17</td>\n",
       "      <td>334.40</td>\n",
       "      <td>310 days 20:49:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Frequency   Amount           Recency\n",
       "0     12346.0          2     0.00 326 days 02:33:00\n",
       "1     12347.0        182  4310.00   2 days 20:58:00\n",
       "2     12348.0         31  1797.24  75 days 23:37:00\n",
       "3     12349.0         73  1757.55  19 days 02:59:00\n",
       "4     12350.0         17   334.40 310 days 20:49:00"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Combining all recency, frequency and monetary parameters\n",
    "RFM = k.merge(monetary, on = \"CustomerID\")\n",
    "RFM = RFM.merge(df, on = \"CustomerID\")\n",
    "RFM.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3V901qSuenpJ6aNWqVVx++eWsWbOGN998k6uvvpqTJ09y5MgRPvzww6q7J53l6SlpCdiwYQOHDh3i8OHDALzxxhv09/ezYcOGinsmdcfTU1IPHTt2jDNnzrBjxw7effddduzYwZkzZzh27FjVXZO6YmhIPXTy5EluueUW9uzZw2WXXcaePXu45ZZbOHnyZNVdk7piaEg9duDAAdavX8+qVatYv349Bw4cqLpLUtcWFRoR8UZEvBoRr0TEWFH7bETsi4jXi/e1RT0i4qGImIiIX0TEFzu2s61o/3pEbOuof6nY/kSxbiymv1IVTpw4wcGDB/nwww85ePAgJ06cqLpLUtcuxpHGYGbe0DHqfj/wQmZuBl4ovgPcCWwuXtuBh2EmZIAHgJuAG4EHZoOmaLO9Y70tF6G/0iduenr6nHdpuerF6amtwOPF58eBuzvqT+SMnwGXRcR64A5gX2YezcxjwD5gS7Hs05n505yZF/xEx7akZaVWq53zLi1Xiw2NBP53RLwcEduL2ucz822A4v1zRX0DcLBj3cmiNl99co66tKysWbOGffv2cebMGfbt28eaNWuq7pLUtcVep/HlzHwrIj4H7IuI/ztP27nGI7KL+kc3PBNY2wE2btw4f4+lCszeOuTqq6+uuivSoizqSCMz3yreDwH/yMyYxDvFqSWK90NF80ngqo7VrwTeWqB+5Rz1ufrxSGYOZObAunXrFvOTpItq1apVnDp1itOnTxMRnD59mlOnTrFqlRMXtTx1/Tc3ItZExH+Y/QzcDuwH9gKzM6C2AU8Xn/cC9xazqG4Gjhenr54Hbo+ItcUA+O3A88Wy9yLi5mLW1L0d25KWhW9/+9sAZ28bcuTIkXPq0nKzmNNTnwf+sZgFewnwPzPzf0XES8BTEdEEfg18rWj/LHAXMAGcAr4OkJlHI+J7wEtFu+9m5tHi8w7gMeBTwHPFS1o2du3aBcCjjz7KBx98QK1W41vf+tbZurTcdB0amfkr4D/PUf9/wO/MUU/gvo/Z1h5gzxz1MaDRbR+lpeDWW2+l3W4zPj7Otddey6233lp1l6SuecNCqYd8cp9WGm+NLvVQo9Fg165dDA4Onq21222Gh4d9cp+WlLK3RncKh9RD4+PjTE5O0mg0qNVqNBoNJicnGR8fr7prUlcMDamHrrjiCoaHh8/e1fbkyZMMDw9zxRVXVNwzqTuOaUg9dOrUKY4fP87x48eBmYcwgbcT0fLlkYbUQ0ePzswen72Yb/Z9ti4tN4aG1GP9/f1s3LiRVatWsXHjRvr7+6vuktQ1T09JPXbmzJmzp6Vm36XlyiMNSVJphoYkqTRDQ5JUmqEh9VhfXx+bNm0iIti0aRN9fX1Vd0nqmgPhUo9NTU05EK4VwyMNqYeKRweUrktLnaEh9dDH3RB0pd0oVP9+GBqSpNIMDUlSaYaGJKk0Q0OSVJqhIUkqzdCQJJVmaEiSSjM0JEmlGRqSpNIMDUlSaYaGJKm0JR8aEbElIn4ZERMRcX/V/ZGkf8+W9K3RI6IG/AD4L8Ak8FJE7M3M16rtmbT4O9WWXd+bG2opWdKhAdwITGTmrwAi4klgK2BoqHJl/jGfLxgMAy1HSz00NgAHO75PAjdV1BetZA9+piebzQc+/YnvkweP92a7Eks/NOb6b9pH/nsWEduB7QAbN27sdZ+0AsV/O1F1Fy6KtWvXcvTBqnuhlWyph8YkcFXH9yuBt85vlJmPAI8ADAwMeMyvC9bNqaJP6ul7nsbSUrLUZ0+9BGyOiGsioh+4B9hbcZ8kYOYf84Ve119/PS+++OI5tRdffJHrr7++1PoGhpaaWOp/KSPiLuC/AzVgT2bunK/9wMBAjo2NfSJ9kxZSq9U4ffo0fX19Z2tTU1OsXr2a6enpCnsmnSsiXs7MgYXaLfUjDTLz2cz8j5n5hYUCQ1pq6vU6o6Oj59RGR0ep1+sV9UhanCUfGtJyNjIyQrPZpN1uMzU1RbvdptlsMjIyUnXXpK4s9YFwaVkbGhoCYHh4mPHxcer1Ojt37jxbl5abJT+mcaEc05CkC7dixjQkSUuHoSFJKs3QkCSVZmhIkkozNCRJpa242VMRcRh4s+p+SHO4HDhSdSekj3F1Zq5bqNGKCw1pqYqIsTJTGqWlzNNTkqTSDA1JUmmGhvTJeaTqDkiL5ZiGJKk0jzQkSaUZGlKPRcSeiDgUEfur7ou0WIaG1HuPAVuq7oR0MRgaUo9l5o+Bo1X3Q7oYDA1JUmmGhiSpNENDklSaoSFJKs3QkHosIlrAT4H/FBGTEdGsuk9St7wiXJJUmkcakqTSDA1JUmmGhiSpNENDklSaoSFJKs3QkCSVZmhIkkozNCRJpf1/45Ywx1BtgEoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f9a80a1cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# outlier treatment for Amount\n",
    "plt.boxplot(RFM.Amount)\n",
    "Q1 = RFM.Amount.quantile(0.25)\n",
    "Q3 = RFM.Amount.quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "RFM = RFM[(RFM.Amount >= Q1 - 1.5*IQR) & (RFM.Amount <= Q3 + 1.5*IQR)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f9a8421cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# outlier treatment for Frequency\n",
    "plt.boxplot(RFM.Frequency)\n",
    "Q1 = RFM.Frequency.quantile(0.25)\n",
    "Q3 = RFM.Frequency.quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "RFM = RFM[(RFM.Frequency >= Q1 - 1.5*IQR) & (RFM.Frequency <= Q3 + 1.5*IQR)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f9a84ce7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# outlier treatment for Recency\n",
    "plt.boxplot(RFM.Recency)\n",
    "Q1 = RFM.Recency.quantile(0.25)\n",
    "Q3 = RFM.Recency.quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "RFM = RFM[(RFM.Recency >= Q1 - 1.5*IQR) & (RFM.Recency <= Q3 + 1.5*IQR)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Recency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>326 days 02:33:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348.0</td>\n",
       "      <td>31</td>\n",
       "      <td>1797.24</td>\n",
       "      <td>75 days 23:37:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349.0</td>\n",
       "      <td>73</td>\n",
       "      <td>1757.55</td>\n",
       "      <td>19 days 02:59:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350.0</td>\n",
       "      <td>17</td>\n",
       "      <td>334.40</td>\n",
       "      <td>310 days 20:49:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12352.0</td>\n",
       "      <td>95</td>\n",
       "      <td>1545.41</td>\n",
       "      <td>36 days 22:13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12353.0</td>\n",
       "      <td>4</td>\n",
       "      <td>89.00</td>\n",
       "      <td>204 days 19:03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12354.0</td>\n",
       "      <td>58</td>\n",
       "      <td>1079.40</td>\n",
       "      <td>232 days 23:39:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12355.0</td>\n",
       "      <td>13</td>\n",
       "      <td>459.40</td>\n",
       "      <td>214 days 23:01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12356.0</td>\n",
       "      <td>59</td>\n",
       "      <td>2811.43</td>\n",
       "      <td>23 days 04:10:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12358.0</td>\n",
       "      <td>19</td>\n",
       "      <td>1168.06</td>\n",
       "      <td>2 days 02:24:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12360.0</td>\n",
       "      <td>129</td>\n",
       "      <td>2662.06</td>\n",
       "      <td>52 days 21:28:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>12361.0</td>\n",
       "      <td>10</td>\n",
       "      <td>189.90</td>\n",
       "      <td>287 days 22:59:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>12363.0</td>\n",
       "      <td>23</td>\n",
       "      <td>552.00</td>\n",
       "      <td>110 days 02:32:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>12364.0</td>\n",
       "      <td>85</td>\n",
       "      <td>1313.10</td>\n",
       "      <td>8 days 02:28:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>12365.0</td>\n",
       "      <td>23</td>\n",
       "      <td>320.69</td>\n",
       "      <td>291 days 22:46:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>12367.0</td>\n",
       "      <td>11</td>\n",
       "      <td>168.90</td>\n",
       "      <td>4 days 20:02:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>12370.0</td>\n",
       "      <td>167</td>\n",
       "      <td>3545.69</td>\n",
       "      <td>51 days 21:59:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>12371.0</td>\n",
       "      <td>63</td>\n",
       "      <td>1887.96</td>\n",
       "      <td>45 days 02:34:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>12372.0</td>\n",
       "      <td>52</td>\n",
       "      <td>1298.04</td>\n",
       "      <td>72 days 00:38:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>12373.0</td>\n",
       "      <td>14</td>\n",
       "      <td>364.60</td>\n",
       "      <td>311 days 23:40:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CustomerID  Frequency   Amount           Recency\n",
       "0      12346.0          2     0.00 326 days 02:33:00\n",
       "2      12348.0         31  1797.24  75 days 23:37:00\n",
       "3      12349.0         73  1757.55  19 days 02:59:00\n",
       "4      12350.0         17   334.40 310 days 20:49:00\n",
       "5      12352.0         95  1545.41  36 days 22:13:00\n",
       "6      12353.0          4    89.00 204 days 19:03:00\n",
       "7      12354.0         58  1079.40 232 days 23:39:00\n",
       "8      12355.0         13   459.40 214 days 23:01:00\n",
       "9      12356.0         59  2811.43  23 days 04:10:00\n",
       "11     12358.0         19  1168.06   2 days 02:24:00\n",
       "13     12360.0        129  2662.06  52 days 21:28:00\n",
       "14     12361.0         10   189.90 287 days 22:59:00\n",
       "16     12363.0         23   552.00 110 days 02:32:00\n",
       "17     12364.0         85  1313.10   8 days 02:28:00\n",
       "18     12365.0         23   320.69 291 days 22:46:00\n",
       "19     12367.0         11   168.90   4 days 20:02:00\n",
       "20     12370.0        167  3545.69  51 days 21:59:00\n",
       "21     12371.0         63  1887.96  45 days 02:34:00\n",
       "22     12372.0         52  1298.04  72 days 00:38:00\n",
       "23     12373.0         14   364.60 311 days 23:40:00"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFM.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standardise all parameters\n",
    "RFM_norm1 = RFM.drop(\"CustomerID\", axis=1)\n",
    "RFM_norm1.Recency = RFM_norm1.Recency.dt.days\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "standard_scaler = StandardScaler()\n",
    "RFM_norm1 = standard_scaler.fit_transform(RFM_norm1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Recency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.070949</td>\n",
       "      <td>-1.041614</td>\n",
       "      <td>2.136422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.375498</td>\n",
       "      <td>1.385298</td>\n",
       "      <td>-0.282050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.631707</td>\n",
       "      <td>1.331702</td>\n",
       "      <td>-0.821629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.711233</td>\n",
       "      <td>-0.590055</td>\n",
       "      <td>1.982257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.159290</td>\n",
       "      <td>1.045238</td>\n",
       "      <td>-0.657828</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Frequency    Amount   Recency\n",
       "0  -1.070949 -1.041614  2.136422\n",
       "1  -0.375498  1.385298 -0.282050\n",
       "2   0.631707  1.331702 -0.821629\n",
       "3  -0.711233 -0.590055  1.982257\n",
       "4   1.159290  1.045238 -0.657828"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFM_norm1 = pd.DataFrame(RFM_norm1)\n",
    "RFM_norm1.columns = ['Frequency','Amount','Recency']\n",
    "RFM_norm1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hopkins Statistics:\n",
    "The Hopkins statistic, is a statistic which gives a value which indicates the cluster tendency, in other words: how well the data can be clustered.\n",
    "\n",
    "- If the value is between {0.01, ...,0.3}, the data is regularly spaced.\n",
    "\n",
    "- If the value is around 0.5, it is random.\n",
    "\n",
    "- If the value is between {0.7, ..., 0.99}, it has a high tendency to cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import NearestNeighbors\n",
    "from random import sample\n",
    "from numpy.random import uniform\n",
    "import numpy as np\n",
    "from math import isnan\n",
    " \n",
    "def hopkins(X):\n",
    "    d = X.shape[1]\n",
    "    #d = len(vars) # columns\n",
    "    n = len(X) # rows\n",
    "    m = int(0.1 * n) # heuristic from article [1]\n",
    "    nbrs = NearestNeighbors(n_neighbors=1).fit(X.values)\n",
    " \n",
    "    rand_X = sample(range(0, n, 1), m)\n",
    " \n",
    "    ujd = []\n",
    "    wjd = []\n",
    "    for j in range(0, m):\n",
    "        u_dist, _ = nbrs.kneighbors(uniform(np.amin(X,axis=0),np.amax(X,axis=0),d).reshape(1, -1), 2, return_distance=True)\n",
    "        ujd.append(u_dist[0][1])\n",
    "        w_dist, _ = nbrs.kneighbors(X.iloc[rand_X[j]].values.reshape(1, -1), 2, return_distance=True)\n",
    "        wjd.append(w_dist[0][1])\n",
    " \n",
    "    H = sum(ujd) / (sum(ujd) + sum(wjd))\n",
    "    if isnan(H):\n",
    "        print(ujd, wjd)\n",
    "        H = 0\n",
    " \n",
    "    return H"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.891398975292978"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hopkins(RFM_norm1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Means with some K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=50,\n",
       "    n_clusters=5, n_init=10, n_jobs=1, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Kmeans with K=5\n",
    "model_clus5 = KMeans(n_clusters = 5, max_iter=50)\n",
    "model_clus5.fit(RFM_norm1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Silhouette Analysis\n",
    "\n",
    "$$\\text{silhouette score}=\\frac{p-q}{max(p,q)}$$\n",
    "\n",
    "$p$ is the mean distance to the points in the nearest cluster that the data point is not a part of\n",
    "\n",
    "$q$ is the mean intra-cluster distance to all the points in its own cluster.\n",
    "\n",
    "* The value of the silhouette score range lies between -1 to 1. \n",
    "\n",
    "* A score closer to 1 indicates that the data point is very similar to other data points in the cluster, \n",
    "\n",
    "* A score closer to -1 indicates that the data point is not similar to the data points in its cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import silhouette_score\n",
    "sse_ = []\n",
    "for k in range(2, 15):\n",
    "    kmeans = KMeans(n_clusters=k).fit(RFM_norm1)\n",
    "    sse_.append([k, silhouette_score(RFM_norm1, kmeans.labels_)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f9ac4b50b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(pd.DataFrame(sse_)[0], pd.DataFrame(sse_)[1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sum of Squared Distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f9b4f7ce80>]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f9b5175630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sum of squared distances\n",
    "ssd = []\n",
    "for num_clusters in list(range(1,21)):\n",
    "    model_clus = KMeans(n_clusters = num_clusters, max_iter=50)\n",
    "    model_clus.fit(RFM_norm1)\n",
    "    ssd.append(model_clus.inertia_)\n",
    "\n",
    "plt.plot(ssd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# analysis of clusters formed\n",
    "RFM.index = pd.RangeIndex(len(RFM.index))\n",
    "RFM_km = pd.concat([RFM, pd.Series(model_clus5.labels_)], axis=1)\n",
    "RFM_km.columns = ['CustomerID', 'Frequency', 'Amount', 'Recency', 'ClusterID']\n",
    "\n",
    "RFM_km.Recency = RFM_km.Recency.dt.days\n",
    "km_clusters_amount = pd.DataFrame(RFM_km.groupby([\"ClusterID\"]).Amount.mean())\n",
    "km_clusters_frequency = pd.DataFrame(RFM_km.groupby([\"ClusterID\"]).Frequency.mean())\n",
    "km_clusters_recency = pd.DataFrame(RFM_km.groupby([\"ClusterID\"]).Recency.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ClusterID</th>\n",
       "      <th>Amount_mean</th>\n",
       "      <th>Frequency_mean</th>\n",
       "      <th>Recency_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1195.977443</td>\n",
       "      <td>53.688953</td>\n",
       "      <td>49.960756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2472.898046</td>\n",
       "      <td>117.600543</td>\n",
       "      <td>38.366848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>327.627693</td>\n",
       "      <td>20.598234</td>\n",
       "      <td>262.737307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>913.691366</td>\n",
       "      <td>114.125683</td>\n",
       "      <td>54.122951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>356.193340</td>\n",
       "      <td>23.263696</td>\n",
       "      <td>57.818846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ClusterID  Amount_mean  Frequency_mean  Recency_mean\n",
       "0          0  1195.977443       53.688953     49.960756\n",
       "1          1  2472.898046      117.600543     38.366848\n",
       "2          2   327.627693       20.598234    262.737307\n",
       "3          3   913.691366      114.125683     54.122951\n",
       "4          4   356.193340       23.263696     57.818846"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([pd.Series([0,1,2,3,4]), km_clusters_amount, km_clusters_frequency, km_clusters_recency], axis=1)\n",
    "df.columns = [\"ClusterID\", \"Amount_mean\", \"Frequency_mean\", \"Recency_mean\"]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f9b0e8f5f8>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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juucd3fBtwLKhzZcCD07xnldU1aqqWjUxMTHO9iVpUeslPJL8iyTPnFwGTgbuBNYC53TDzgE+0y2vBd7cXXV1AvC9ycNbkqS519dhqyOBG5NM9vCHVfX5JLcB1yd5G3A/8MZu/DrgdGAr8APgrXPfsiRpUi/hUVX3AC+eov5d4KQp6gWcOwetSZJGMN8u1ZUkLQCGhySpmeEhSWpmeEiSmhkekqRmhockqZnhIUlq1ufcVpI07/3uuz/bdwuz7rwPvma/38M9D0lSM/c8pM6Jl53Ydwtj8aV3fKnvFvQk5J6HJKmZ4SFJamZ4SJKaGR6SpGaGhySpmeEhSWpmeEiSmhkekqRmhockqZnhIUlqZnhIkpoZHpKkZoaHJKmZ4SFJamZ4SJKaGR6SpGaGhySpmeEhSWpmeEiSmhkekqRmhockqZnhIUlqZnhIkpotqPBIcmqSu5JsTXJB3/1I0mK1YMIjyYHAR4HTgGOBs5Ic229XkrQ4LZjwAI4HtlbVPVX1Q+A64Iyee5KkRWkhhccS4IGh9W1dTZI0x1JVffcwkiRvBE6pqv/YrZ8NHF9V7xgasxpY3a0+H7hrzhvd0+HAd/puYp7wu9jN72I3v4vd5sN38ZyqmtjboIPmopNZsg1YNrS+FHhweEBVXQFcMZdN7U2SDVW1qu8+5gO/i938Lnbzu9htIX0XC+mw1W3AiiTHJDkEOBNY23NPkrQoLZg9j6raleQ84CbgQGBNVW3uuS1JWpQWTHgAVNU6YF3ffTSaV4fReuZ3sZvfxW5+F7stmO9iwZwwlyTNHwvpnIckaZ4wPMbEqVR2S7ImyY4kd/bdS5+SLEtyS5ItSTYneWffPfUlyVOTfDXJX3bfxX/vu6e+JTkwydeTfK7vXkZheIyBU6ns4Srg1L6bmAd2Ae+uqp8BTgDOXcT/u3gMeFVVvRhYCZya5ISee+rbO4EtfTcxKsNjPJxKZUhVfRF4uO8++lZV26vqa93yowz+Q7EoZ0mogb/vVg/uHov2BGySpcAvAP+z715GZXiMh1OpaEZJlgMvAb7Sbyf96Q7TbAJ2AOuratF+F8CHgd8Aftx3I6MyPMYjU9QW7b+q9HhJngHcAJxfVY/03U9fqupHVbWSwWwRxyd5Yd899SHJq4EdVbWx715aGB7jsdepVLQ4JTmYQXBcW1Wf6ruf+aCq/g74Aov3vNiJwGuT3MfgEPerkvxBvy3tneExHk6loj0kCXAlsKWqLum7nz4lmUhyaLf8NODfAd/st6t+VNWFVbW0qpYz+G/Fn1XVm3pua68MjzGoql3A5FQqW4DrF/NUKkk+Cfw/4PlJtiV5W9899eRE4GwG/7Lc1D1O77upnhwF3JLkdgb/2FpfVQviElUNeIe5JKmZex6SpGaGhySpmeEhSWpmeEiSmhkekqRmhoc0gyQ/meS6JH+d5BtJ1iV53r7OEJzkLUmevQ/bvTfJf+6Wr0pybzcj7V8luSaJ099oThke0jS6m/puBL5QVc+tqmOB9wBH7sfbvgVoCo8kU/3i5693M9I+H/g6g3smDtmPvqQmhoc0vVcC/1RVH5ssVNUmhia97PYkfndo/XNJXtFN+ndVkjuT3JHkXUneAKwCru1uEHxakpcmuTXJxiQ3JTmqe58vJPmtJLcymKp7St3stB8C/obBTwBIc8LwkKb3QmBfJ6tbCSypqhdW1YuAT1TVHwMbgP/QTQi4C7gMeENVvRRYA1w89B6HVtXLq+qDI3ze14Cf3sdepWZT7Q5L2n/3AD+V5DLgfwN/MsWY5zMIqPWDI2QcCGwfev2PGj5vqpmcpbExPKTpbQbesJcxu3j8HvxTAarqb5O8GDgFOBf4ZeBXn7BtgM1V9bJp3vv7Db2+BLi5Yby0XzxsJU3vz4CnJPm1yUKSfw08Z2jMfcDKJAckWcbgVyRJcjhwQFXdAPwmcFw3/lHgmd3yXcBEkpd12xyc5AUtDWbgPzGYaPDzjX+ftM/c85CmUVWV5PXAh5NcAPwjg7A4f2jYl4B7gTuAOxmce4DBL0d+IsnkP9Au7J6vAj6W5B+AlzHYs7k0yb9k8P/HDzPY49mb307ym8DTgS8Dr+x+8liaE86qK0lq5mErSVIzw0OS1MzwkCQ1MzwkSc0MD0lSM8NDktTM8JAkNTM8JEnN/j+eHmfa+mJgdQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f9b50804e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df.ClusterID, y=df.Amount_mean)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f9afa05a90>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f9b325aa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df.ClusterID, y=df.Frequency_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f9b5096198>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JTYaEJKnJkJAkNRkSkqQmQ0KS1GRISJKaDAlJUpMhIUlqMiQkSU2GhCSpaepCIsnzk9yZZGOSi4euR5IWsqkKiSQHAO8BXgAcB5yb5Lhhq5KkhWuqQgI4CdhYVd+oqh8AHwbOGrgmSVqwpi0kFgP3jq1v6tokSQNIVQ1dw39IcjZwRlX9crf+cuCkqnrNTv1WAiu71WOBO/dpobM7HHhg6CKmhN/FDn4XI34PO0zLd/GUqlq0u06P3heV9LAJWDK2fhRw386dqmoVsGpfFTUXSdZW1Yqh65gGfhc7+F2M+D3s8Ej7LqZtuOlvgGOSHJ3kIOAc4PqBa5KkBWuqjiSqaluSC4BPAQcAV1TV7QOXJUkL1lSFBEBV3QDcMHQde2Cqhr8G5nexg9/FiN/DDo+o72KqTlxLkqbLtJ2TkCRNEUNiLzmNyEiSK5JsSXLb0LUMLcmSJDcl2ZDk9iQXDl3TUJI8NslXkvxt91387tA1DS3JAUm+muQTQ9cyF4bEXnAakR9zJfD8oYuYEtuA11fVTwInA+cv4P8uvg88t6qOB5YDz09y8sA1De1CYMPQRcyVIbF3nEakU1WfA/556DqmQVVtrqqbu+WHGP2DsCBnDqiR73arB3avBXsiNMlRwH8HPjB0LXNlSOwdpxHRLiVZBpwAfHnYSobTDa+sB7YAa6pqwX4XwDuB3wJ+NHQhc2VI7J3M0rZg/0rSj0vyBOBa4HVV9Z2h6xlKVW2vquWMZlA4Kckzh65pCEn+B7ClqtYNXUsfhsTemdM0Ilp4khzIKCCurqqPDl3PNKiqbwGfYeGeuzoVeGGSuxkNTT83yZ8OW9LuGRJ7x2lE9DBJAnwQ2FBVbx+6niElWZTkkG75ccDPA18btqphVNUlVXVUVS1j9G/FX1XVLw5c1m4ZEnuhqrYBM9OIbACuWajTiCT5EPBF4Ngkm5K8euiaBnQq8HJGfymu715nDl3UQI4EbkpyC6M/qtZU1SPi0k+NeMe1JKnJIwlJUpMhIUlqMiQkSU2GhCSpyZCQJDUZEhKQ5CeSfDjJ3ye5I8kNSZ6+p7PaJnllkifvwXZvTvKb3fKVSe7qZlD9uyRXJXHaF+1ThoQWvO7mt+uAz1TV06rqOOANwBF7sdtXAr1CIslsT4q8qJtB9Vjgq4zuOThoL+qSejEkJDgd+GFVvW+moarWMzZ5Y3dk8O6x9U8kOa2bvO7KJLcluTXJbyR5CbACuLq7ke5xSZ6d5LNJ1iX5VJIju/18JsnvJfksoymkZ9XNpvoO4J8YTU0v7ROGhATPBPZ00rXlwOKqemZVPQv446r6CLAWeFk3sd024F3AS6rq2cAVwKVj+zikqp5TVZfN4fNuBv7rHtYq9Tbb4a2kufsG8NQk7wL+AvjLWfocyyiI1oxGtjgA2Dz28z/v8XmzzTwsTYwhIcHtwEt202cbP37k/ViAqnowyfHAGcD5wEuBX9pp2wC3V9UpjX1/r0etJwA39ugv7RWHmyT4K+AxSX5lpiHJTwNPGetzN7A8yaOSLGH0VEKSHA48qqquBX4HOLHr/xDwxG75TmBRklO6bQ5M8ow+BWbktYwmzPtkz99P2mMeSWjBq6pK8mLgnUkuBv6NUSi8bqzbF4C7gFuB2xidG4DRkwj/OMnMH1yXdO9XAu9L8q/AKYyOVC5P8p8Z/X/3TkZHMLvz1iS/A/wn4EvA6d2jcqV9wllgJUlNDjdJkpoMCUlSkyEhSWoyJCRJTYaEJKnJkJAkNRkSkqQmQ0KS1PTvO43m752BFdQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f9b276b1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df.ClusterID, y=df.Recency_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heirarchical Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ce8b221a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# heirarchical clustering\n",
    "mergings = linkage(RFM_norm1, method = \"single\", metric='euclidean')\n",
    "dendrogram(mergings)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ce9682bb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mergings = linkage(RFM_norm1, method = \"complete\", metric='euclidean')\n",
    "dendrogram(mergings)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusterCut = pd.Series(cut_tree(mergings, n_clusters = 5).reshape(-1,))\n",
    "RFM_hc = pd.concat([RFM, clusterCut], axis=1)\n",
    "RFM_hc.columns = ['CustomerID', 'Frequency', 'Amount', 'Recency', 'ClusterID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#summarise\n",
    "RFM_hc.Recency = RFM_hc.Recency.dt.days\n",
    "km_clusters_amount = \tpd.DataFrame(RFM_hc.groupby([\"ClusterID\"]).Amount.mean())\n",
    "km_clusters_frequency = \tpd.DataFrame(RFM_hc.groupby([\"ClusterID\"]).Frequency.mean())\n",
    "km_clusters_recency = \tpd.DataFrame(RFM_hc.groupby([\"ClusterID\"]).Recency.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ClusterID</th>\n",
       "      <th>Amount_mean</th>\n",
       "      <th>Frequency_mean</th>\n",
       "      <th>Recency_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>252.966698</td>\n",
       "      <td>22.707291</td>\n",
       "      <td>142.854733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1919.512313</td>\n",
       "      <td>92.709751</td>\n",
       "      <td>50.852608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>867.327807</td>\n",
       "      <td>56.955626</td>\n",
       "      <td>73.369255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2990.865636</td>\n",
       "      <td>118.920530</td>\n",
       "      <td>44.165563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-1183.350000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>248.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ClusterID  Amount_mean  Frequency_mean  Recency_mean\n",
       "0          0   252.966698       22.707291    142.854733\n",
       "1          1  1919.512313       92.709751     50.852608\n",
       "2          2   867.327807       56.955626     73.369255\n",
       "3          3  2990.865636      118.920530     44.165563\n",
       "4          4 -1183.350000        7.800000    248.200000"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([pd.Series([0,1,2,3,4]), km_clusters_amount, km_clusters_frequency, km_clusters_recency], axis=1)\n",
    "df.columns = [\"ClusterID\", \"Amount_mean\", \"Frequency_mean\", \"Recency_mean\"]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ce8be237f0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ce8d05e2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting barplot\n",
    "sns.barplot(x=df.ClusterID, y=df.Amount_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ce8af43780>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ce8be23d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df.ClusterID, y=df.Frequency_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ce8adadb70>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ce944ae240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df.ClusterID, y=df.Recency_mean)"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}