{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "title: How to perform (simple) customer segmentation with clustering\n",
    "author: Dr Ghita Berrada\n",
    "bibliography: ../../../../references/DS101.bib\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of clustering is to find groups (of similar points) in the data. One use case of clustering is customer segmentation i.e grouping customers together according to similar properties (e.g similar spending patterns) in order to maximize their value to the business.\n",
    "Here, we'll demonstrate a very simple case of that."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load a very simple dataset representing a variety of mall customer attributes after importing the prerequisite set of libraries for our demo. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np # linear algebra\n",
    "import matplotlib.pyplot as plt # library for plots\n",
    "import seaborn as sns # library for plots\n",
    "\n",
    "import missingno as msno # library for the exploration of missingness patterns in data\n",
    "\n",
    "from sklearn.cluster import KMeans # import for k-means algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"Mall_Customers.csv\")  #loading our dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then check the content of our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Genre</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual_Income_(k$)</th>\n",
       "      <th>Spending_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID   Genre  Age  Annual_Income_(k$)  Spending_Score\n",
       "0           1    Male   19                  15              39\n",
       "1           2    Male   21                  15              81\n",
       "2           3  Female   20                  16               6\n",
       "3           4  Female   23                  16              77\n",
       "4           5  Female   31                  17              40"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Age</th>\n",
       "      <th>Annual_Income_(k$)</th>\n",
       "      <th>Spending_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>100.500000</td>\n",
       "      <td>38.850000</td>\n",
       "      <td>60.560000</td>\n",
       "      <td>50.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>57.879185</td>\n",
       "      <td>13.969007</td>\n",
       "      <td>26.264721</td>\n",
       "      <td>25.823522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.750000</td>\n",
       "      <td>28.750000</td>\n",
       "      <td>41.500000</td>\n",
       "      <td>34.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>100.500000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>61.500000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>150.250000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CustomerID         Age  Annual_Income_(k$)  Spending_Score\n",
       "count  200.000000  200.000000          200.000000      200.000000\n",
       "mean   100.500000   38.850000           60.560000       50.200000\n",
       "std     57.879185   13.969007           26.264721       25.823522\n",
       "min      1.000000   18.000000           15.000000        1.000000\n",
       "25%     50.750000   28.750000           41.500000       34.750000\n",
       "50%    100.500000   36.000000           61.500000       50.000000\n",
       "75%    150.250000   49.000000           78.000000       73.000000\n",
       "max    200.000000   70.000000          137.000000       99.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now check our data for any missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 2500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no missing values in our data! Hurray! This should make our life much easier..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This count of NULL values per column confirms that there are indeed no missing values in our data. We're good to go!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID            0\n",
       "Genre                 0\n",
       "Age                   0\n",
       "Annual_Income_(k$)    0\n",
       "Spending_Score        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our dataset contains more data points from female customers..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Genre\n",
       "Female    112\n",
       "Male       88\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(by='Genre')['Age'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = sns.countplot(x = 'Genre',data = data)\n",
    "sizes=[]\n",
    "for p in s.patches:\n",
    "    height = p.get_height()\n",
    "    sizes.append(height)\n",
    "    s.text(p.get_x()+p.get_width()/2.,\n",
    "            height + 3,\n",
    "            '{:1.2f}%'.format(height/len(data)*100),\n",
    "            ha=\"center\", fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(data=data, x='Age',binwidth=5)\n",
    "plt.title(\"Distribution of Mall Customers' Age\")\n",
    "plt.xlabel(\"Mall Customers' Age\")\n",
    "plt.ylabel('Frequency')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(data=data, x='Annual_Income_(k$)')\n",
    "plt.title(\"Distribution of Mall Customers' Annual Income (k$)\")\n",
    "plt.xlabel(\"Mall Customers' Annual Income (k$)\")\n",
    "plt.ylabel('Frequency')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(data=data, x='Spending_Score')\n",
    "plt.title(\"Distribution of Mall Customers' Spending Scores\")\n",
    "plt.xlabel(\"Mall Customers' Spending Scores\")\n",
    "plt.ylabel('Frequency')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will cluster (i.e group) our data using the k-means algorithm. K-means requires the number of clusters as a parameter. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we proceed, we drop duplicate data points from our dataset and only keep the income and spending score variables to allow easy visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop_duplicates(inplace=True)\n",
    "# using only Spending_Score and income variable for easy visualisation\n",
    "X = data.iloc[:, [3, 4]].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We determine the number of parameters for k-means with the \"elbow method\".\n",
    "\n",
    "- In this method, we vary the number of clusters `k`, apply k-means to the data and measure what we call the within-cluster-sum-of-squares (WCSS) i.e the sum of the squared distance between each point in a cluster and the centroid of the cluster it belongs to. \n",
    "- We then plot the WCSS against `k`: the optimal value of clusters (i.e the number of clusters we need to choose for k-means) corresponds to the value of `k` starting from which the decrease rate of the WCSS curve inflects and the graph starts to look like a straight line."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we initialize the K-means runs with the K-means++ initialization (see details in [arthur2007k])."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "K-means initialization is important! Two random initializations can result in totally different clustering outcomes\n",
    "\n",
    "E.g:\n",
    "\n",
    "Random initialization 1:\n",
    "\n",
    "![](figures/kmeans_init.png)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random initialization 2:\n",
    "\n",
    "![](figures/kmeans_init2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using the elbow method to find the optimal number of clusters\n",
    "# https://iq.opengenus.org/k-means-vs-k-means-p/\n",
    "wcss = []\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42,n_init='auto')\n",
    "    kmeans.fit(X)\n",
    "    # inertia method returns wcss for that model\n",
    "    wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.lineplot(x=range(1, 11), y=wcss,marker='o',color='red')\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot above shows that the WCSS curve inflects and flattens for a `k` value of 5: so the optimal number of clusters is 5. This is the parameter we'll pass to the k-means algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting K-Means to the dataset\n",
    "kmeans = KMeans(n_clusters = 5, init = 'k-means++', random_state = 42,n_init='auto')\n",
    "y_kmeans = kmeans.fit_predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualise our results!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the clusters\n",
    "plt.figure(figsize=(15,7))\n",
    "sns.scatterplot(x=X[y_kmeans == 0, 0], y=X[y_kmeans == 0, 1], color = 'yellow', label = 'Cluster 1',s=50)\n",
    "sns.scatterplot(x=X[y_kmeans == 1, 0], y=X[y_kmeans == 1, 1], color = 'blue', label = 'Cluster 2',s=50)\n",
    "sns.scatterplot(x=X[y_kmeans == 2, 0], y=X[y_kmeans == 2, 1], color = 'green', label = 'Cluster 3',s=50)\n",
    "sns.scatterplot(x=X[y_kmeans == 3, 0], y=X[y_kmeans == 3, 1], color = 'grey', label = 'Cluster 4',s=50)\n",
    "sns.scatterplot(x=X[y_kmeans == 4, 0], y=X[y_kmeans == 4, 1], color = 'orange', label = 'Cluster 5',s=50)\n",
    "sns.scatterplot(x=kmeans.cluster_centers_[:, 0], y=kmeans.cluster_centers_[:, 1], color = 'red', \n",
    "                label = 'Centroids',s=300,marker=',')\n",
    "plt.grid(False)\n",
    "plt.title('Clusters of customers')\n",
    "plt.xlabel('Annual Income (k$)')\n",
    "plt.ylabel('Spending Score (1-100)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X = data[['Age','Annual_Income_(k$)']].values #age and income\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "wcss = []\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42,n_init='auto')\n",
    "    kmeans.fit(X)\n",
    "    # inertia method returns wcss for that model\n",
    "    wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.lineplot(x=range(1, 11), y=wcss,marker='o',color='red')\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting K-Means to the dataset\n",
    "kmeans = KMeans(n_clusters = 4, init = 'k-means++', random_state = 42,n_init='auto')\n",
    "y_kmeans = kmeans.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/A5C/y9YaVf33AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the clusters\n",
    "plt.figure(figsize=(15,7))\n",
    "sns.scatterplot(x=X[y_kmeans == 0, 0], y=X[y_kmeans == 0, 1], color = 'yellow', label = 'Cluster 1',s=50)\n",
    "sns.scatterplot(x=X[y_kmeans == 1, 0], y=X[y_kmeans == 1, 1], color = 'blue', label = 'Cluster 2',s=50)\n",
    "sns.scatterplot(x=X[y_kmeans == 2, 0], y=X[y_kmeans == 2, 1], color = 'green', label = 'Cluster 3',s=50)\n",
    "sns.scatterplot(x=X[y_kmeans == 3, 0], y=X[y_kmeans == 3, 1], color = 'grey', label = 'Cluster 4',s=50)\n",
    "sns.scatterplot(x=kmeans.cluster_centers_[:, 0], y=kmeans.cluster_centers_[:, 1], color = 'red', \n",
    "                label = 'Centroids',s=300,marker=',')\n",
    "plt.grid(False)\n",
    "plt.title('Clusters of customers')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Annual Income (k$)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}