Heirarical Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
       "    <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\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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset= pd.read_csv('Customers.csv')\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X= dataset.iloc[:,[3,4]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creating a demdogram\n",
    "import scipy.cluster.hierarchy as sch\n",
    "\n",
    "dendogram= sch.dendrogram(sch.linkage(X, method=\"ward\"))\n",
    "plt.title(\"Dendogram\")\n",
    "plt.xlabel('Coustomer')\n",
    "plt.ylabel('Eucledian Distance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Clustering\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "hc= AgglomerativeClustering(n_clusters=3, affinity='euclidean', linkage='ward')\n",
    "\n",
    "#predict the data and cluster it\n",
    "y_hc= hc.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Visualization of data\n",
    "plt.scatter(X[y_hc==0,0], X[y_hc==0,1], s=100, c='cyan', label='1st Cluster')\n",
    "plt.scatter(X[y_hc==1,0], X[y_hc==1,1], s=100, c='green', label='2nd Cluster')\n",
    "plt.scatter(X[y_hc==2,0], X[y_hc==2,1], s=100, c='red', label='3rd Cluster')\n",
    "\n",
    "plt.title('Cluster of Customer')\n",
    "plt.xlabel('Annual Salary(Ks)')\n",
    "plt.ylabel('Spending(1 to 100)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}