micro_projects/Hierarchial Clustering - Mall csv ML.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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing mall dataset with pandas\n",
    "dataset = pd.read_csv('../datasets/machine_learning_a-z/P14-Hierarchical-Clustering/Hierarchical_Clustering/Mall_Customers.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>195</td>\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",
       "      <td>196</td>\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",
       "      <td>197</td>\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",
       "      <td>198</td>\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",
       "      <td>199</td>\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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.iloc[:,[3,4]].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CREATE DANDOGRAM "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.cluster.hierarchy as sch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": [
    "dendrogram = sch.dendrogram(sch.linkage(X, method = 'ward'))\n",
    "plt.title('Dendrogram')\n",
    "plt.xlabel('Customers')\n",
    "plt.ylabel('Eucladean Distances')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "hc = AgglomerativeClustering(n_clusters=5, affinity='euclidean', linkage='ward')\n",
    "y_hc = hc.fit_predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VISUALIZE THE CLUSTERS "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    "plt.scatter(X[y_hc ==0, 0], X[y_hc == 0, 1], s=100, c='red', label= 'Cluster 1')\n",
    "plt.scatter(X[y_hc ==1, 0], X[y_hc == 1, 1], s=100, c='blue', label= 'Cluster 2')\n",
    "plt.scatter(X[y_hc ==2, 0], X[y_hc == 2, 1], s=100, c='green', label= 'Cluster 3')\n",
    "plt.scatter(X[y_hc ==3, 0], X[y_hc == 3, 1], s=100, c='cyan', label= 'Cluster 4')\n",
    "plt.scatter(X[y_hc ==4, 0], X[y_hc == 4, 1], s=100, c='magenta', label= 'Cluster 5')\n",
    "plt.title('Clusters of clients')\n",
    "plt.xlabel('Annual Income k$')\n",
    "plt.ylabel('Spending Score (1-100)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-23-f32356ef9562>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-23-f32356ef9562>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    y_hc == 0,1?\u001b[0m\n\u001b[0m               ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "y_hc == 0,1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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