Hierarchical_Clutsering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading csv\n",
    "\n",
    "df=pd.read_csv('Customers.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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 (1-100)</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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>196</td>\n",
       "      <td>Female</td>\n",
       "      <td>35</td>\n",
       "      <td>120</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>197</td>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "      <td>126</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>198</td>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>126</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>199</td>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>137</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>200</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>137</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     CustomerID   Genre  Age  Annual Income (k$)  Spending Score (1-100)\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\n",
       "..          ...     ...  ...                 ...                     ...\n",
       "195         196  Female   35                 120                      79\n",
       "196         197  Female   45                 126                      28\n",
       "197         198    Male   32                 126                      74\n",
       "198         199    Male   32                 137                      18\n",
       "199         200    Male   30                 137                      83\n",
       "\n",
       "[200 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataframe contains 200 data points and 5 features \n",
    "\n",
    "Problem statement : We need to cluster the customers on the basis their Annual income and spending\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualising Dendrogram\n",
    "\n",
    "Dendogram is tree like structure(Hierarchi) which represent the merging of groups\n",
    "\n",
    "It helps us to visualise how many clusters to take"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": [
    "# Dendogram\n",
    "\n",
    "import scipy.cluster.hierarchy as sch\n",
    "\n",
    "cluster_visualising=sch.dendrogram(sch.linkage(df.iloc[:,[3,4]].values,method='ward'))\n",
    "plt.title('Dendrogram')\n",
    "plt.xlabel('Customers')\n",
    "plt.ylabel('Euclidean distances')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we observe the above dendrogram , it group the customers according to min eculidean distance\n",
    "\n",
    "How to select no clusters from dendogram\n",
    "\n",
    "We select largest vertical line which can not cut by horizontal line "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can choose no of cluster is eqaul 5 from dendrogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgglomerativeClustering(n_clusters=5)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# AgglomerativeClustering Model initialization\n",
    "\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "clustering_model=AgglomerativeClustering(n_clusters = 5, affinity = 'euclidean', linkage = 'ward')\n",
    "\n",
    "clustering_model.fit(df.iloc[:,[3,4]].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting clusters\n",
    "clustering_prediction=clustering_model.fit_predict(df.iloc[:,[3,4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3,\n",
       "       4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 1,\n",
       "       4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 0, 2, 0, 2,\n",
       "       1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 1, 2, 0, 2, 1, 2, 0, 2, 0, 2, 0, 2,\n",
       "       0, 2, 0, 2, 0, 2, 1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2,\n",
       "       0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2,\n",
       "       0, 2], dtype=int64)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustering_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": [
    "# Visualising clusters\n",
    "\n",
    "plt.scatter(df.iloc[:,[3,4]].values[clustering_prediction == 0, 0], df.iloc[:,[3,4]].values[clustering_prediction == 0, 1], s = 100, c = 'green', label = 'Cluster 1')\n",
    "plt.scatter(df.iloc[:,[3,4]].values[clustering_prediction == 1, 0], df.iloc[:,[3,4]].values[clustering_prediction == 1, 1], s = 100, c = 'red', label = 'Cluster 2')\n",
    "plt.scatter(df.iloc[:,[3,4]].values[clustering_prediction == 2, 0], df.iloc[:,[3,4]].values[clustering_prediction == 2, 1], s = 100, c = 'blue', label = 'Cluster 3')\n",
    "plt.scatter(df.iloc[:,[3,4]].values[clustering_prediction == 3, 0], df.iloc[:,[3,4]].values[clustering_prediction == 3, 1], s = 100, c = 'yellow', label = 'Cluster 4')\n",
    "plt.scatter(df.iloc[:,[3,4]].values[clustering_prediction == 4, 0], df.iloc[:,[3,4]].values[clustering_prediction == 4, 1], s = 100, c = 'orange', label = 'Cluster 5')\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": "markdown",
   "metadata": {},
   "source": [
    "If we observe the above clustering graph there is no over lap between clusters and each clusters have certain distance from each other \n",
    "\n",
    "So our model doing well clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5529945955148897"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.silhouette_score(df.iloc[:,[3,4]].values, clustering_prediction , metric='euclidean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5782207452369452"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.davies_bouldin_score(df.iloc[:,[3,4]].values, clustering_prediction )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "243.07142892964563"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.calinski_harabasz_score(df.iloc[:,[3,4]].values, clustering_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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