ml_blueprint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ML Model Blueprint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>There are two main objectives in the given task:\n",
    "1. Customer segmentation based on browsing behaviour\n",
    "2. Target those segmented customers for effective cross selling of products</b>\n",
    "\n",
    "For this task, I want to complete both the objectives with a single algorithm. Hence, I am proposing a single unsupervised ML model that can fulfill both the objectives at the same time. As the output of the model, it will segment customers based on the two objectives, that is, their browsing behaviour and whether that customer can be a candidate for cross selling or not.\n",
    "\n",
    "Based on the features of the UCI Online Retail dataset, I feel like unsupervised ML algorithm is the way to go for customer segmentation as there is no direct labelled features to categorize the customers by. However, since I am doing the clustering based on two objectives simultaneously, the selection of features is very important. <br>\n",
    "\n",
    "<b>Why unsupervised ML algorithm?</b><br>\n",
    "In this task, the goal is unsupervised. We need to segment custom groups into some sort of structure based on their behaviour as well as identify the cross selling opportunity in the customer segments. So, as there are no clear and straight forward target labels in our dataset to segment customers based on the two aforementioned objectives, I am using unsupervised ML algorithm.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Additional Feature Engineering for ML Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ML algorithm that I used is called K Prototype. It is an unsupervised clustering algorithm that will help to generate different customer clusters based on the features and thus perform customer segmentation based on both the customer behaviour as well as the cross selling opportunity.\n",
    "\n",
    "<b> Why use Clustering technique? </b> <br>\n",
    "In business analytics, clustering has widespread application as it helps to group observations of similar kinds into smaller groups within the larger population. One major problem every business faces is to organize huge data into meaningful insights. Clustering techniques are handy in such situation.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *RFMPCG - A modified RFM analysis specifically to fulfill the objectives of this task*\n",
    "\n",
    "To complete the given business objective, it is needed to segment customers based on browsing behaviour as well as identify customer segments for effective cross selling. One of the most popular, easy-to-use, and effective segmentation methods to enable marketers to analyze customer behavior is RFM analysis. The three metrics in RFM analysis are Recency, Frequency and Monetary Value. So, it is a common practice to design unsupervised ML models based on the RFM metrics for customer segmentation. <br>\n",
    "However, for the task in our hand, I want to design an integrated model that can categorize customer segments not only based on their browsing behaviour but also considering the cross selling opportunity more precisely. Moreover, both the tasks at hand are interrelated and have many common features for which idea of designing such a model is reasonable. However, for our model, only the RFM metrics are not enough to get the clarity in information and segment customers based on those two perspective.<br>\n",
    "\n",
    "For this reason, a modified RFM analysis is proposed to bring clarity into the information for effective use of this algoithm in customer segmentation. Based on the given dataset for the task and our objectives, the features chosen in addition to RFM are Geography, Product Variety and Cancelled Order. After analyzing the dataset in great detail, I found these three additional features to be complimentary for our purpose of both analyzing customer behaviour as well cross selling scopes. This analysis method is termed as RFMPCG. The feature engineering steps are performed accordingly where the use of the features are justified as well.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"cleaned_dataset.csv\")\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Monetary Value*\n",
    "Monetary value of the transaction helps us understand what the size of the customer's wallet is and their purchasing power. In addition, it is an important feature to analyzing both upsale and cross sale scopes as customers with higher purchase capability may avail the opportunity if we try to cross sell to them. The average amount spent by each customer is taken as the monetary value in our case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Monetary</th>\n",
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       "    <tr>\n",
       "      <th>CustomerID</th>\n",
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      "text/plain": [
       "             Monetary\n",
       "CustomerID           \n",
       "12347.0     25.239670\n",
       "12348.0     55.542222\n",
       "12349.0     20.152083\n",
       "12350.0     17.537500\n",
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      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "cus_data = df.groupby('CustomerID')[['TotalPrice']].mean() # Average amount spent\n",
    "cus_data.rename(columns={'TotalPrice':'Monetary'},inplace=True)\n",
    "cus_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Frequency*\n",
    "Frequency is important as we get to know how active the customer is, which in turn partly explains the browsing behavior. Moreover, the more active and frequent a customer is, the more loyal he/she is to. The frequency is calculated from the number of Invoice count for each customer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
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   "outputs": [
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       "      <th></th>\n",
       "      <th>Monetary</th>\n",
       "      <th>Frequency</th>\n",
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       "      <th>CustomerID</th>\n",
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      "text/plain": [
       "             Monetary  Frequency\n",
       "CustomerID                      \n",
       "12347.0     25.239670        182\n",
       "12348.0     55.542222         27\n",
       "12349.0     20.152083         72\n",
       "12350.0     17.537500         16\n",
       "12352.0     17.779405         84"
      ]
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     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "cus_data['Frequency'] = df.groupby('CustomerID')['InvoiceNo'].count()\n",
    "cus_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Recency*\n",
    "Recency of the purchase indicates whether a customer is active, dormant, or churned. Based on their browsing behaviour, the recency metric of the customers can also be inferred proving to be vital for our customer segmentation. The recency value is extracted from the InvoiceDate in the following way by substracting the date of transaction from the most recent InvoiceDate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['InvoiceDate'] = pd.to_datetime(df['InvoiceDate'])\n",
    "max_date = max(df['InvoiceDate'])\n",
    "df['diff'] = max_date - df['InvoiceDate']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Monetary</th>\n",
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       "      <th>Recency</th>\n",
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       "   CustomerID   Monetary  Frequency  Recency\n",
       "0     12347.0  25.239670        182        1\n",
       "1     12348.0  55.542222         27       74\n",
       "2     12349.0  20.152083         72       18\n",
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      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime as dt\n",
    "cus_data['Recency'] = df.groupby('CustomerID')['diff'].min().dt.days\n",
    "cus_data = cus_data.reset_index()\n",
    "cus_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Product Variety*\n",
    "Product variety can help explain customers' willingness to explore. This is related to browsing behavior as well and indicates the number of products a customer seeks to buy from the store. More importantly, this is a vital feature to analyze the cross selling opportunities for the customers. This is becauses people who tend to buy variety of products can be the most suitable candidates providing they have enough money and loyalty to the business."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cus_data['ProductVariety']=df.groupby('CustomerID')['Description'].transform(lambda x:x.nunique())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *CancellationRate*\n",
    "From the analysis of data done earlier, I found that there is a significant amount of orders that have been cancelled. One approach could have been to drop these transactions as it did not generate any revenue for the company. However, almost all the cancelled transactions are from UK where 90% of the transactions have taken place. So, in another perspective, these cancelled transaction can help the business identify the nature of the unreliable customers. Moreover, some of these customers if targetted by proper marketing techniques may become future potential customers for the company to look for. Because of these reasons, this categorical feature is considered in our customer segmentation analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cus_data['CancellationRate']=df.groupby('CustomerID')['CancelledOrder'].transform(lambda x:x.mode()[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Geography*\n",
    "The customer behaviour and cross selling scopes both may vary based on geography. Moreover, there is a huge imbalance between home and international transactions in the dataset. K Prototype algorithm suffers greatly in case of data imbalance. So, it is better to segment the international customers seperately and not on the basis of UK customers. As a result, this feature is not directly considered while implementing our ML model. Based on geography, I seperated the transactions into home and abroad at first and then use the rest of the other features directly in our ML algorithm for customer segmentation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cus_data['Place']= df['Place']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cus_data.to_csv('final.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ML Model Implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K Prototype\n",
    "In K Prototype algorithm, data points are allocated to the cluster that has the most similar looking reference point. It attempts to find the dissimilarity between data points and then groups the points with less dissimilarity into a cluster. One key highlight of this algorithm is that it can be used for both numerical and categorical features in a dataset. It also works very fast.\n",
    "\n",
    "<b> Why not K Means Clustering Algorithm? </b> <br>\n",
    "KMeans Clustering is works very great when the features are only continuous variables. For example, if I only did RFM analysis, I could have used KMeans. Since I want to design an algorithm based on RFMPCG analysis, that takes account of both my objective functions, I am unable to use KMeans. Because my RFMPCG metric contains both numerical as well as categorical features. The reason is K-means uses Euclidean distance to measure the similarity of objects and there aren’t Euclidean distances between data segmented by categorical features. <br>\n",
    "\n",
    "One key highlight of K Prototype algorithm is that it can be used for both numerical and categorical features in a dataset. It also works very fast. The dissimilarity measure for numeric attributes is the square Euclidean distance whereas the similarity measure on categorical attributes is the number of matching attributes between objects and cluster prototypes. The algorithm has the following 3 steps:\n",
    "1. Initial prototype selection : I selected the Cao approach. It selects the prototypes based on the density of the data point and the dissimilarity value\n",
    "2. Initial allocation\n",
    "3. Re-allocation\n",
    "\n",
    "The initial prototype selection is done for all the data points. This keeps on repeating until there is no re-allocation of data points needed.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***At first based on geography, the final features are seperated. It is because there is huge imbalance in the dataset in terms of geography. Most of the customers are from UK. K Prototype algorithm is hugely affected by imbalanced dataset like other clustering algorithms. To solve this issue, I am making a seperate customer segmentation for home country and another one for all the abroad countries. Moreover, this will also make the customer segmentation more precise according to our needs.*** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Domestic Customer Segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing necessary libraries and the dataset\n",
    "import pandas as pd, numpy as np, matplotlib.pyplot as plt, seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from kmodes.kprototypes import KPrototypes\n",
    "from sklearn.metrics import silhouette_score\n",
    "import matplotlib.patches as mpatch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID          4160\n",
       "Monetary            4115\n",
       "Frequency            458\n",
       "Recency              347\n",
       "ProductVariety       123\n",
       "CancellationRate       2\n",
       "Place                  1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cus_data = pd.read_csv('final.csv')\n",
    "cus_data['Place']= df['Place']\n",
    "cus_data = cus_data[cus_data['Place'] == 'Home']\n",
    "cus_data.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "   CustomerID   Monetary  Frequency  Recency  ProductVariety  CancellationRate\n",
       "0     12347.0  25.239670        182        1              24                 0\n",
       "1     12348.0  55.542222         27       74              24                 0\n",
       "2     12349.0  20.152083         72       18              24                 0\n",
       "3     12350.0  17.537500         16      309              24                 0\n",
       "4     12352.0  17.779405         84       35              24                 0"
      ]
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     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "cus_data.drop([\"Place\"], axis = 1, inplace = True)\n",
    "cus_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Here, only the numerical features are scaled in order to avoid the negative effects of feature magnitudes and also speed up the process. Rest of the other preprocessing steps have been performed earlier.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "Scaler = StandardScaler()\n",
    "cus_data.iloc[:,1:5] = Scaler.fit_transform(cus_data.iloc[:,1:5]) #only numerical values\n",
    "syms = cus_data.iloc[:,0].values.astype(str)\n",
    "X = cus_data.iloc[:,1:].values.astype(object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0569643260137559, 1.0180629897630806, -1.3600756790338595,\n",
       "        -0.906346776626451, 0.0],\n",
       "       [1.0109586304126326, -0.40230362283996857, 0.114500079050101,\n",
       "        -0.906346776626451, 0.0],\n",
       "       [-0.07046554784352427, 0.12072525153459863, -0.7311208288472313,\n",
       "        -0.906346776626451, 0.0],\n",
       "       ...,\n",
       "       [-0.342460538426005, -0.7540733291878386, -1.040859531094062,\n",
       "        0.46670258577396884, 0.0],\n",
       "       [-1.0546140296724633, 3.528715415778481, -1.2180843360421945,\n",
       "        0.46670258577396884, 0.0],\n",
       "       [0.1461288309344717, 0.09944906135177584, -0.2970104869600315,\n",
       "        0.46670258577396884, 0.0]], dtype=object)"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Finding the optimal value for K to select no. of clusters*\n",
    "To check the optimal number of clusters, I have checked the following:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Silhoutte Score: It is a metric used to calculate the goodness of a clustering technique. The value of this score ranges from -1 to 1.  This score is calculated using the mean intra-cluster distance and the mean nearest-cluster distance for each sample. The more positive the silhoutte score, the better the clustering. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of clusters: 2\n",
      "  cost:  13222.063404332192\n",
      "  average silhouette score:  0.45709488561467077\n",
      "number of clusters: 3\n",
      "  cost:  10309.50008745059\n",
      "  average silhouette score:  0.5002906721928431\n",
      "number of clusters: 4\n",
      "  cost:  8132.789274812967\n",
      "  average silhouette score:  0.5062079255757251\n",
      "number of clusters: 5\n",
      "  cost:  6546.916731302087\n",
      "  average silhouette score:  0.5115216853672968\n",
      "number of clusters: 6\n",
      "  cost:  5489.81013362881\n",
      "  average silhouette score:  0.3542827783461176\n",
      "number of clusters: 7\n",
      "  cost:  4475.387543619727\n",
      "  average silhouette score:  0.4158600414529169\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#finding the optimal cluster_number k\n",
    "for n in range(2,8):\n",
    "    kproto = KPrototypes(n_clusters = n, init = 'Cao')\n",
    "    clusters = kproto.fit_predict(X, categorical = [4])\n",
    "    silhouette = silhouette_score(X[:,:4],clusters)\n",
    "    print('number of clusters:', n)\n",
    "    print('  cost: ',kproto.cost_)\n",
    "    print('  average silhouette score: ',silhouette)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Elbow Plot: The Elbow Plot is another way of finding the optimal value of clusters. The elbow plot is calculated between the cost and cluster number (k). Here, the cost is a combined similarity measure for both numeric and categorical variables calculated between the objects and the cluster prototypes as K Prototype contains both types of variables. This calculation helps to come to the optimal number of clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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 num_clusters in list(range(1,10)):\n",
    "    kproto = KPrototypes(n_clusters = num_clusters, init = 'Cao')\n",
    "    kproto.fit_predict(X, categorical = [4])\n",
    "    cost.append(kproto.cost_)\n",
    "    \n",
    "plt.plot(cost, marker = '8')\n",
    "plt.grid()\n",
    "plt.xlabel(\"Number of Clusters\")\n",
    "plt.xticks(np.arange(1,9,1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on both these techniques, I found that clusters number 4 or 5 are suitable in our case for customer segmentation. Even though the silhoutte score for 5 clusters is the best, when running the algorithm with clusters =5, it did not give any additional insights about the data compared to running the algorithm with clusters = 4. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster Centers:\n",
      " [[-2.32954195e-02  6.82544499e-02 -5.12318056e-01 -1.35381142e-01\n",
      "   0.00000000e+00]\n",
      " [-6.17712263e-02  3.54586493e-02 -5.73102852e-02  5.19552926e+00\n",
      "   0.00000000e+00]\n",
      " [ 7.69074555e-02 -2.89262086e-01  1.54420781e+00 -1.51144385e-01\n",
      "   0.00000000e+00]\n",
      " [-1.70355509e-01  2.73241496e+01 -8.98460677e-01  1.00308313e+00\n",
      "   0.00000000e+00]]\n"
     ]
    }
   ],
   "source": [
    "#clustering with kprototypes with k = 4\n",
    "kproto = KPrototypes(n_clusters = 4, init = 'Cao')\n",
    "clusters = kproto.fit_predict(X, categorical = [4])\n",
    "print('Cluster Centers:\\n', kproto.cluster_centroids_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\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></th>\n",
       "      <th>Monetary</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Recency</th>\n",
       "      <th>ProductVariety</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th>CancellationRate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
       "      <th>0.0</th>\n",
       "      <td>42.177043</td>\n",
       "      <td>27.877734</td>\n",
       "      <td>246.292247</td>\n",
       "      <td>104.079523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>13.654493</td>\n",
       "      <td>24.600000</td>\n",
       "      <td>254.400000</td>\n",
       "      <td>5.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>0.0</th>\n",
       "      <td>15.218044</td>\n",
       "      <td>4735.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>213.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>0.0</th>\n",
       "      <td>26.489677</td>\n",
       "      <td>99.657407</td>\n",
       "      <td>84.972222</td>\n",
       "      <td>1153.962963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>0.0</th>\n",
       "      <td>30.829081</td>\n",
       "      <td>104.932759</td>\n",
       "      <td>39.254720</td>\n",
       "      <td>107.842663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>28.434204</td>\n",
       "      <td>150.176471</td>\n",
       "      <td>43.764706</td>\n",
       "      <td>7.529412</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Monetary    Frequency     Recency  ProductVariety\n",
       "Cluster CancellationRate                                                    \n",
       "0       0.0               42.177043    27.877734  246.292247      104.079523\n",
       "        1.0               13.654493    24.600000  254.400000        5.600000\n",
       "1       0.0               15.218044  4735.000000    0.200000      213.000000\n",
       "2       0.0               26.489677    99.657407   84.972222     1153.962963\n",
       "3       0.0               30.829081   104.932759   39.254720      107.842663\n",
       "        1.0               28.434204   150.176471   43.764706        7.529412"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#scaling back to original values and retrieving all attributes\n",
    "Clustered = pd.DataFrame(data=Scaler.inverse_transform(cus_data.iloc[:,1:5]),columns=['Monetary', 'Frequency', 'Recency', 'ProductVariety'])\n",
    "#Clustered['Place'] = X[:,4]\n",
    "Clustered['CancellationRate'] = X[:,4]\n",
    "Clustered['CustomerID'] = syms\n",
    "Clustered['Cluster'] = clusters\n",
    "Clustered.groupby(['Cluster', 'CancellationRate']).count()['Frequency']\n",
    "Clustered.groupby(['Cluster', 'CancellationRate']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cluster  CancellationRate\n",
       "0        0.0                 1006\n",
       "         1.0                    5\n",
       "1        0.0                    5\n",
       "2        0.0                  108\n",
       "3        0.0                 3019\n",
       "         1.0                   17\n",
       "Name: Frequency, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Clustered.groupby(['Cluster', 'CancellationRate']).count()['Frequency']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Monetary</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Recency</th>\n",
       "      <th>ProductVariety</th>\n",
       "      <th>CancellationRate</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4060</th>\n",
       "      <td>12.986652</td>\n",
       "      <td>221.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18145.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4061</th>\n",
       "      <td>16.035000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18146.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4062</th>\n",
       "      <td>25.277143</td>\n",
       "      <td>7.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18147.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4063</th>\n",
       "      <td>38.255000</td>\n",
       "      <td>16.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18149.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4064</th>\n",
       "      <td>15.312973</td>\n",
       "      <td>37.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18150.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4155</th>\n",
       "      <td>18.075000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18280.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4156</th>\n",
       "      <td>11.545714</td>\n",
       "      <td>7.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18281.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4157</th>\n",
       "      <td>11.850769</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18282.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4158</th>\n",
       "      <td>2.757109</td>\n",
       "      <td>754.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18283.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4159</th>\n",
       "      <td>27.134857</td>\n",
       "      <td>70.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18287.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Monetary  Frequency  Recency  ProductVariety CancellationRate  \\\n",
       "4060  12.986652      221.0     12.0           166.0                0   \n",
       "4061  16.035000       10.0    140.0           166.0                0   \n",
       "4062  25.277143        7.0     45.0           166.0                0   \n",
       "4063  38.255000       16.0     50.0           166.0                0   \n",
       "4064  15.312973       37.0     31.0           166.0                0   \n",
       "...         ...        ...      ...             ...              ...   \n",
       "4155  18.075000       10.0    277.0           162.0                0   \n",
       "4156  11.545714        7.0    180.0           162.0                0   \n",
       "4157  11.850769       13.0      7.0           162.0                0   \n",
       "4158   2.757109      754.0      3.0           162.0                0   \n",
       "4159  27.134857       70.0     42.0           162.0                0   \n",
       "\n",
       "     CustomerID  Cluster  \n",
       "4060    18145.0        0  \n",
       "4061    18146.0        3  \n",
       "4062    18147.0        0  \n",
       "4063    18149.0        0  \n",
       "4064    18150.0        0  \n",
       "...         ...      ...  \n",
       "4155    18280.0        3  \n",
       "4156    18281.0        3  \n",
       "4157    18282.0        0  \n",
       "4158    18283.0        0  \n",
       "4159    18287.0        0  \n",
       "\n",
       "[100 rows x 7 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Clustered.tail(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *Model Evaluation & Remarks*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 763.5x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#comparison plots\n",
    "sns.pairplot(Clustered.drop(columns=['CancellationRate','CustomerID']), hue='Cluster')\n",
    "plt.suptitle('Scatter Matrix Within Clusters', fontsize = 15, y = 1.05)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "K Prototype algorithms are usually evaluated based on elbow plot and silhoutte scores. The silhoutte score helps to decide the goodness of the clustering. Based on values suggested by elbow plot and silhoutte score and as well as running simulations for different number of clusters, I found that clusters equal to 4 or 5 are suitable in our case for customer segmentation. Even though the silhoutte score for 5 clusters is the best, when running the algorithm with clusters =5, it did not give any additional insights about the data compared to running the algorithm with clusters = 4. However, running with clusters = 4 helped to identify some outlier customers that were not identified with cluster = 3. So, the resulting conclusion is that for domestic customers, we will seperate customers into 4 categories keeping in mind both their behaviour and cross selling opportunity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of Domestic Customer Segments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![<caption>](c.JPG)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Same Steps Repeated for International Customers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### International Customer Segmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Another segmentation is performed only for international customers other than UK***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID          196\n",
       "Monetary            196\n",
       "Frequency           115\n",
       "Recency             116\n",
       "ProductVariety       14\n",
       "CancellationRate      1\n",
       "Place                 1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cus_data['Place']= df['Place']\n",
    "cus_data = pd.read_csv('final.csv')\n",
    "cus_data['Place']= df['Place']\n",
    "\n",
    "cus_data = cus_data[cus_data['Place'] == 'Abroad']\n",
    "cus_data.nunique()"
   ]
  },
  {
   "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",
       "    }\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>Monetary</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Recency</th>\n",
       "      <th>ProductVariety</th>\n",
       "      <th>CancellationRate</th>\n",
       "      <th>Place</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>12378.0</td>\n",
       "      <td>18.316256</td>\n",
       "      <td>219</td>\n",
       "      <td>129</td>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>Abroad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>12379.0</td>\n",
       "      <td>20.471538</td>\n",
       "      <td>39</td>\n",
       "      <td>81</td>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>Abroad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>12380.0</td>\n",
       "      <td>25.055842</td>\n",
       "      <td>101</td>\n",
       "      <td>21</td>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>Abroad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>12381.0</td>\n",
       "      <td>17.657674</td>\n",
       "      <td>86</td>\n",
       "      <td>4</td>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>Abroad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>12383.0</td>\n",
       "      <td>17.442632</td>\n",
       "      <td>95</td>\n",
       "      <td>184</td>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>Abroad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CustomerID   Monetary  Frequency  Recency  ProductVariety  \\\n",
       "26     12378.0  18.316256        219      129             114   \n",
       "27     12379.0  20.471538         39       81             114   \n",
       "28     12380.0  25.055842        101       21             114   \n",
       "29     12381.0  17.657674         86        4             114   \n",
       "30     12383.0  17.442632         95      184             114   \n",
       "\n",
       "    CancellationRate   Place  \n",
       "26                 0  Abroad  \n",
       "27                 0  Abroad  \n",
       "28                 0  Abroad  \n",
       "29                 0  Abroad  \n",
       "30                 0  Abroad  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cus_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "cus_data.drop(\"Place\", axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Rest of the steps are as like before. Numerical features are scaled and silhoutte coefficients are obtained***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of clusters: 2\n",
      "  cost:  593.1904384864591\n",
      "  average silhouette score:  0.5582841929577947\n",
      "number of clusters: 3\n",
      "  cost:  430.6639377225505\n",
      "  average silhouette score:  0.573641612545377\n",
      "number of clusters: 4\n",
      "  cost:  296.0035825555282\n",
      "  average silhouette score:  0.5273865503521059\n",
      "number of clusters: 5\n",
      "  cost:  151.791861392701\n",
      "  average silhouette score:  0.5648509914842175\n",
      "number of clusters: 6\n",
      "  cost:  129.09175703825656\n",
      "  average silhouette score:  0.5316094644608413\n",
      "number of clusters: 7\n",
      "  cost:  107.38376704329593\n",
      "  average silhouette score:  0.5392957677780632\n"
     ]
    }
   ],
   "source": [
    "#scaling the numerical features for clustering\n",
    "Scaler = StandardScaler()\n",
    "cus_data.iloc[:,1:5] = Scaler.fit_transform(cus_data.iloc[:,1:5]) #only numerical values\n",
    "syms = cus_data.iloc[:,0].values.astype(str)\n",
    "X = cus_data.iloc[:,1:].values.astype(object)\n",
    "\n",
    "\n",
    "\n",
    "#finding the optimal cluster_number k\n",
    "for n in range(2,8):\n",
    "    kproto = KPrototypes(n_clusters = n, init = 'Cao')\n",
    "    clusters = kproto.fit_predict(X, categorical = [4])\n",
    "    silhouette = silhouette_score(X[:,:4],clusters)\n",
    "    print('number of clusters:', n)\n",
    "    print('  cost: ',kproto.cost_)\n",
    "    print('  average silhouette score: ',silhouette)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Elbow Plot***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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 num_clusters in list(range(1,7)):\n",
    "    kproto = KPrototypes(n_clusters = num_clusters, init = 'Cao')\n",
    "    kproto.fit_predict(cus_data, categorical = [4])\n",
    "    cost.append(kproto.cost_)\n",
    "    \n",
    "plt.plot(cost, marker = '8')\n",
    "plt.grid()\n",
    "plt.xlabel(\"Number of Clusters\")\n",
    "plt.xticks(np.arange(1,9,1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster Centers:\n",
      " [[ 0.01969023  0.01511732 -0.03944968 -0.33859956  0.        ]\n",
      " [-0.16408526 -0.1259777   0.32874731  2.82166303  0.        ]]\n"
     ]
    }
   ],
   "source": [
    "#clustering with kprototypes with k = 2\n",
    "kproto = KPrototypes(n_clusters = 2, init = 'Cao')\n",
    "clusters = kproto.fit_predict(X, categorical = [4])\n",
    "print('Cluster Centers:\\n', kproto.cluster_centroids_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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></th>\n",
       "      <th>Monetary</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Recency</th>\n",
       "      <th>ProductVariety</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th>CancellationRate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th>0.0</th>\n",
       "      <td>25.472935</td>\n",
       "      <td>295.590909</td>\n",
       "      <td>126.545455</td>\n",
       "      <td>1717.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>0.0</th>\n",
       "      <td>41.389356</td>\n",
       "      <td>93.827586</td>\n",
       "      <td>94.517241</td>\n",
       "      <td>165.626437</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Monetary   Frequency     Recency  ProductVariety\n",
       "Cluster CancellationRate                                                   \n",
       "0       0.0               25.472935  295.590909  126.545455     1717.636364\n",
       "1       0.0               41.389356   93.827586   94.517241      165.626437"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#scaling back to original values and retrieving all attributes\n",
    "Clustered = pd.DataFrame(data=Scaler.inverse_transform(cus_data.iloc[:,1:5]),columns=['Monetary', 'Frequency', 'Recency', 'ProductVariety'])\n",
    "#Clustered['Place'] = X[:,4]\n",
    "Clustered['CancellationRate'] = X[:,4]\n",
    "Clustered['CustomerID'] = syms\n",
    "Clustered['Cluster'] = clusters\n",
    "Clustered.groupby(['Cluster', 'CancellationRate']).count()['Frequency']\n",
    "Clustered.groupby(['Cluster', 'CancellationRate']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cluster  CancellationRate\n",
       "0        0.0                  22\n",
       "1        0.0                 174\n",
       "Name: Frequency, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Clustered.groupby(['Cluster', 'CancellationRate']).count()['Frequency']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 763.5x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#comparison plots\n",
    "sns.pairplot(Clustered.drop(columns=['CancellationRate','CustomerID']), hue='Cluster')\n",
    "plt.suptitle('Scatter Matrix Within Clusters', fontsize = 15, y = 1.05)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Number of customers per cluster\n",
    "\n",
    "font_title = {\n",
    "        'color':  '#104C6C',\n",
    "        'weight': 'normal',\n",
    "        'size': 16,\n",
    "        }\n",
    "\n",
    "\n",
    "font_label = {\n",
    "        'color':  '#104C6C',\n",
    "        'weight': 'normal',\n",
    "        'size': 13,\n",
    "        }\n",
    "plt.figure(figsize=(8,6))\n",
    "ax = Clustered['Cluster'].value_counts().plot(kind='bar')\n",
    "ax.set_xticklabels(['Promising','Slipping','Loyal','Outliers'])\n",
    "ax.set_ylabel('Number of Customers',font_label)\n",
    "ax.set_xlabel('Clusters',font_label)\n",
    "ax.set_title(\"Customer Distribution\",font_title)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on silhoutte score, cluster = 3 has the best score while elbow plot suggests clusters should be equal to 1. Running simulations for different number of clusters, I found that clusters equal to 1 or 2 are appropriate. The international customer segmentation is found lacking compared to the domestic one due to the lack of international customers in the dataset. However, running simulations for clusters = 2 helped to generate a little more extra insight in terms of cross selling opportunitues for foreign customers. So, the resulting conclusion is that for international customers, we will seperate customers into 2 categories keeping in mind both their behaviour and cross selling opportunity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of International Customer Segments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![<caption>](d.jpg)\n"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}