Culstering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0088af11-c8c6-4243-b01e-9d819cf85447",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1788b6d3-90e8-47bf-807f-39364aafe3bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('Mall_Customers.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d7f8ba51-4992-4a48-98f1-916ce281c1dd",
   "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": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1d1cec5e-6383-4433-95ca-929b218f91c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.iloc[:,3:5].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bd7eb52c-a7a9-4bb1-99e6-f5fb3c72ed44",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anoconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Anoconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Anoconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Anoconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Anoconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Anoconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Anoconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Anoconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Anoconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Anoconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "list1 = []\n",
    "for i in range(1,11):\n",
    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
    "    kmeans.fit(X)\n",
    "    list1.append(kmeans.inertia_)\n",
    "plt.plot(range(1,11),list1)\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "218e4495-cb1d-4a83-93e7-8b7703b554cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anoconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "kmeans = KMeans(n_clusters = 5, init = 'k-means++', random_state = 42)\n",
    "y_kmeans = kmeans.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "96a2483b-de64-429c-866e-96d7eed2c1c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "superviced = dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "736b4fd2-f31f-4bac-9403-70d801694117",
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "superviced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5004efeb-4f72-4a68-baa8-c66c06c989ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "superviced['Cluter_group'] = y_kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "28ed0ffc-d92a-4c5e-8e94-3dde7380eca2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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",
       "      <th>Cluter_group</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",
       "      <td>4</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",
       "      <td>2</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",
       "      <td>4</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",
       "      <td>2</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",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\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",
       "      <td>1</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",
       "      <td>3</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",
       "      <td>1</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",
       "      <td>3</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 6 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",
       "     Cluter_group  \n",
       "0               4  \n",
       "1               2  \n",
       "2               4  \n",
       "3               2  \n",
       "4               4  \n",
       "..            ...  \n",
       "195             1  \n",
       "196             3  \n",
       "197             1  \n",
       "198             3  \n",
       "199             1  \n",
       "\n",
       "[200 rows x 6 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "superviced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "964bef45-5714-4653-90b3-73e4d1ad0313",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Annual Income (k$)'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=superviced.columns[3]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e44bb16e-0d4c-4880-ba2a-e1e347c16e7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anoconda3\\Lib\\site-packages\\seaborn\\regression.py:598: UserWarning: legend_out is deprecated from the `lmplot` function signature. Please update your code to pass it using `facet_kws`.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600.736x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "fecet = sns.lmplot(data=superviced, x=superviced.columns[3] , y=superviced.columns[4],hue=superviced.columns[5],fit_reg=False,legend=True,  legend_out=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d3f0bb2-2b08-41d2-b91a-b5a62dcb3e96",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}