hc unsupervised ml.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=pd.read_csv('C:/Users/dheer/Desktop/Deep_Learning_A_Z/K_Means/Mall_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$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID   Genre  Age  Annual Income (k$)  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"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=dataset.iloc[:, [3,4]].values\n",
    "type(x)"
   ]
  },
  {
   "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#using dendogram to find optimal number of  clusters\n",
    "import scipy.cluster.hierarchy as sch\n",
    "dendrogram=sch.dendrogram(sch.linkage(x,method='ward'))\n",
    "#ward=minimize the variace within the clusters\n",
    "plt.title('Dendograms')\n",
    "plt.xlabel('Customers')\n",
    "plt.ylabel('Euclidean distance')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "hc=AgglomerativeClustering(n_clusters=5,affinity='euclidean',linkage='ward')\n",
    "y=hc.fit_predict(x)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualising the results\n",
    "plt.scatter(x[y==0,0],x[y==0,1],s=100, c='red',label='mature')\n",
    "plt.scatter(x[y==1,0],x[y==1,1],s=100, c='blue',label='standard')\n",
    "plt.scatter(x[y==2,0],x[y==2,1],s=100, c='green',label='target')\n",
    "plt.scatter(x[y==3,0],x[y==3,1],s=100, c='yellow',label='fools')\n",
    "plt.scatter(x[y==4,0],x[y==4,1],s=100, c='black',label='sensible')\n",
    "plt.title('clusters of clients')\n",
    "plt.xlabel('annual income')\n",
    "plt.ylabel('spending_score')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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