{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c9c7d82e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d209e5d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "mall_df = pd.read_excel(\"C:/Users/gertr/Desktop/DATA ANALYTICS/PERSONAL PROJECTS/CLUSTERING(WINE)/Mall_Customers.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6ff11432",
   "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": [
    "mall_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "34f18b76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 5)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mall_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f88ac86c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID                0\n",
       "Genre                     0\n",
       "Age                       0\n",
       "Annual Income (k$)        0\n",
       "Spending Score (1-100)    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mall_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9973f51f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Check for duplicates\n",
    "mall_df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ac739411",
   "metadata": {},
   "outputs": [],
   "source": [
    "## rename Genre as Gender\n",
    "mall_df.rename(columns={'Genre':'Gender'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f3114fb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</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  Gender  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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mall_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca0e20d4",
   "metadata": {},
   "source": [
    "# UNIVARIATE ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "efc4bf45",
   "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>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>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>100.500000</td>\n",
       "      <td>38.850000</td>\n",
       "      <td>60.560000</td>\n",
       "      <td>50.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>57.879185</td>\n",
       "      <td>13.969007</td>\n",
       "      <td>26.264721</td>\n",
       "      <td>25.823522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.750000</td>\n",
       "      <td>28.750000</td>\n",
       "      <td>41.500000</td>\n",
       "      <td>34.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>100.500000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>61.500000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>150.250000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CustomerID         Age  Annual Income (k$)  Spending Score (1-100)\n",
       "count  200.000000  200.000000          200.000000              200.000000\n",
       "mean   100.500000   38.850000           60.560000               50.200000\n",
       "std     57.879185   13.969007           26.264721               25.823522\n",
       "min      1.000000   18.000000           15.000000                1.000000\n",
       "25%     50.750000   28.750000           41.500000               34.750000\n",
       "50%    100.500000   36.000000           61.500000               50.000000\n",
       "75%    150.250000   49.000000           78.000000               73.000000\n",
       "max    200.000000   70.000000          137.000000               99.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mall_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8d2fc83",
   "metadata": {},
   "source": [
    "###### From the above statistics, we can tell that annual income and spending score has somewhat of a normal distribution since its mean and median are really close."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "842e8632",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(mall_df['Annual Income (k$)'],kde=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "615dd4de",
   "metadata": {},
   "source": [
    "#### In order to visualize the distribution of all the other numeric variables, a for loop will be efficient since it allows to go through each item in a list or any kind of structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d8184e9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CustomerID', 'Gender', 'Age', 'Annual Income (k$)',\n",
       "       'Spending Score (1-100)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### To easily copy and paste the columns needed for the loop below\n",
    "mall_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0cc6aaac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+fXu5e+W2Ya4u6vZLZ3nZb6LpN4Cdcy8AL5jZg845rZ8iJ6S8spmxWSkUZmvs74m67eIZPLphP//nia384tNLfZcjMTLYPuBUM7vHzFaa2bNHbjGtTOJaTXMHNS2dzCvJ9V1KXBqblcpfnj+NJ9+q5rXdmi84UQ02gP8f8Drwd8DX+txEjmlzZTPhJGP2eI39PVmfO28qRTmpfPfxcnojWjUjEQ02gHuccz9zzq11zq0/cotpZRK3unsjbDvYwoxxWaRq3t+Tlp4S4lsfnsumA01aQTlBDTaAHzezL5pZsZkVHLnFtDKJW9urW+jqjXDaBHU/nKqrFhRz3oyx3PXkNqqbO3yXI0NssAF8E9Euh1eA9cFtXayKkvj2VmUz+RnJlOSm+S4l7pkZ37tmHl29Eb67otx3OTLEBhXAzrkpx7hNjXVxEn/qD3dS1dTBvJJczDT2dyiUjc3kSxdM548bq3hua43vcmQIDTQOGAAzu/FY251zvxzaciTeba6MTrwze5SvejzUvnD+VFZsrOTrj2zkidvOY4ym9UwIg+2COKPP7TzgTuDqGNUk8SqUzNaqZqYVZpGRMqi/7TJIqeEQP75+MU3t3XzjkY04p1ERiWBQvyXOuS/3fWxmucC/x6QiiVsZM5fT0RPhtBJd+RYLc4pzuOOy2Xx3RTm/WrPXdzkyBE52Oso2YMZQFiLxL3vR5eSkhSnVopsx85lzyjh/ZiF/v6Kc5MIpvsuRUzTYPuDHgSP/84SAOcDvYlWUxJ9tB1tIK53P/Ik6+RZLZsZdH1vA1T95mdbr/ietnT1kpqq7J14N9jv3gz4f9wB7nHP7Y1CPxKlfrt5NpLuT03TpccyNy07j3puWcsX/PcTjGyu5bslEwiGtrRCPBjsM7QVgK9EZ0fIBTdcv72hq7+bRDQdo2/Ii6brybVjMm5BL3YofUN3cycryaiI6KReXBrsixseBtcDHgI8Da8xM01EKAI+s3097dy8tG1b4LmVUad/xKudNH8uOmsPRENZ8EXFnsF0Q3wLOcM7VAJhZIfA08PtYFSbxIRJx/Purezh9cj6PVu/0Xc6os2RyPr3O8crOeiIRx4dOG6/J7+PIYDuOko6Eb6D+BF4rCezFHbVU1LVy43KtkODLGWUF77SE/7ipis6eXt8lySANtgX8hJk9CTwcPP4E8KfYlCTx5L5VFRRmp3L5vGLfpYxqSybnEw4Zz2+v5T9e28eV84t1tVwcGGhNuOlmdo5z7mvAL4AFwEJgNXDPMNQnI1h5ZTMv7ajjM+eUkRLWP0S+LZiYx58tnkhXT4TfrttHeWWzrpgb4Qb6rfkR0ALgnHvUOXe7c+4rRFu/P4ptaTLS/dtLu8hMCfGpM0d594MleVkY81gm5KfzyWWlFGan8tSWah7ZcICGVg1aGqkG6oIoc85tPHqjc26dmZXFpiSJB5WH2nn8zUpuOruM3PRk3+X45SJeFsc83sKYWalhrlsykbcqm1n1dh2/XrOHuSU5LJ1coO/VCDNQAPc3oWv6UBYi8eX+VRU4opfGyshjZsybkMvUwkxe3dVAeWUzbx1oZkZRFvNKcpmYn64rFkeAgQL4NTP7vHPu3/puNLPPEp2U/bjMbBLwS2A8EAHucc79OFhJ47dAGbAb+LhzrvHkyhcfmtq7eXjtXq5cUMzEfM37MJJlpIS5cPY4lk0p4PW9jWw+0Mz26sNkp4WZWZTNlLGZFOekkaSha14MFMB/A/ynmX2KdwN3KZACfGSA1/YAX3XObTCzbGC9mT0F3Aw845z7vpndAdwBfOMk6xcPHnplN61dvdzyAc3JHy+yUsOcN6OQs6aOYVdtK1sONrNhbyPr9zSSGk5iYn46JbnplOSlU5idqrHEw6TfAHbOVQNnm9kFwLxg8x+dcwMuSe+cqwKqgo9bzGwLMAG4Bvhg8LSHgOdRAMeNpvZu7n1pF5fMLdK8D3EoOZTErPHZzBqfTWdPL3sb2thd18aBQ+3srG0FIJRkjM9JoyQvjZK8dIpz00gN6xLzWBjsfMDPAc+d7E6CE3aLgTVAURDOOOeqzGzcyb6vDL8HX95Nc0cPt12k2UjjXWo4xIxx2cwYF129pLWzh8qmdqoOdVDZ1M76PY28trsRAwqzU5mQF20hT8jX6Z+hEvN57MwsC3gE+BvnXPNgO/7N7BbgFoDS0tLYFRgjk0ons3/f8E+aPXFSKfv2xmYJ86b2bu5dtYtL5xYxTyseJ5zM1PB7Arm7N0JVUwcHDrVT2djOxgNNvL7vEAYUffIfuW9VBZfOLWKS5n8+aTENYDNLJhq+v3bOPRpsrjaz4qD1Wwwcc5VB59w9BBd7LF26NO5Gk+/ft3dEDU0aCg+8XEFLRw+3qvU7KiSHkigtyHhngv2eSISa5k721LexqiaL760o53sryplbnMN1p0/kI4snkJ+Z4rnq+BKzy5cs2tS9D9jinLu7z6ceI7rMPcH9H2JVgwydxtaud1o8av2OTuGkJEry0lk+bQxVD3yZF772Qf7uw3MIJRnfXVHOmf/7GW59+HU27W/yXWrciGUL+Bzg08AmM3sj2PY/gO8DvwuGsu0lOsWljHA/fmYHrZ09fDWGLWyJL5PHZPK586byufOmUl7ZzO/W7eP36/fz2JuVLJ86hr++YDrnTB+j8cb9iFkAO+dWAcf7yl8Uq/3K0NtZe5hfvbqH65eVMmu8lpuX95tbksOdV5/G7ZfO5Ldr93H/yxXccN8alk8dw9cum8WS0nzfJY5ImkFFBvSPf9pCWnKI2y+Z6bsUGeFy0pL5/Aem8vzXPsi3r5rL9uoWPvrTV/jSbzZwsKnDd3kjjgJY+vXy23U8vaWGv75gOmM1vaEMUmo4xGfOmcKLX7+AWy+awcryai76v89z70u76OmN+C5vxFAAy3F19UT43opyJuana84HOSmZqWFuv2QmT33lAyybUsDf/3ELH/vFanbVHvZd2oigAJbj+vkLO9l6sIU7rzqNNC22Kadg8phM7r/5DH58/SJ21bZyxT+/xEOv7B718xUrgOWYdlS38JNnd3DVwhIunlvkuxxJAGbGNYsmsPIrH+DMKWP49mNv8de/2UBLR7fv0rxRAMv79EYcX/v9RrJSw9x51Vzf5UiCKcpJ48HPnME3L5/Nk29Vc/W/vMzWg82+y/JCASzvc/+qCt7Yd4g7rz5N64pJTJgZXzh/Gg9//ixaO3v4s5++wrNbq32XNewUwPIe6/c08E9PbOVDpxVx9cIS3+VIgls2pYDHv3wuUwoz+dxD63jg5QrfJQ0rBbC8o+5wJ1/89QYm5Kfzf65bqCuYZFgU5aTxuy8s56I5RXzn8ej8EqPl5JwCWIBov++tD7/OobZufvap07V2mAyrjJQwP7/hdG4+u4z7VlXwzUc30RtJ/BCO+XSUMvI55/jeinJe2VnPXdctYG5Jju+SZBQKJRnfvmou2WlhfvLs2xzu7OGHn1hEcihx24kKYOGnz+/kwVd289lzp/CxpZN8lyOjmJnx1UtnkZUa5h//eysO+OfrFyfsEkkK4FHuFy/s5K4nt3H1whK+dcUc3+WIAPCF86eRZMY//GkLaeEQd123ICEXDlUAj1KRiOMHK7fx0+d3cuWCYu7++MKE/AGX+PX5D0ylvbuXu5/aTlpyEn9/7byEOzGsAB6Fmju6+cbvN/Lfmw/y52eW8t2rTyOcwP1sEr++fOF02rp6+fkLOxmXncZtFyfWaiwJHcC+1mU7lp7eCLWHO6lr6aK9p5fO7l56I46UcBIp4SSyUsLkZaSQn5FMagznXXhlZx13PLKJA4fa+bsPz+Gz505JuFaFJA4z4xuXzaK2pZMfPr2dCfnpfOUj53j7vR7qNRcTOoB9rcsG0bXZWjt72HqwhZ21h6lu7qDvqJpwkhFKMrp6Ihw92CYnLcz4nDSKctMYn5PGuOzUU26h7qo9zN1PbWfFxiomj8ngt7ecxdKyglN6T5HhYGb840fnU93cwR2PbKQuKZ+7Vz7lpZahXnMxoQPYl+b2bsZcfhv3v1xBxMG47FQWl+ZTnBsN0/SUEOGkaKA65+iJOFo6ejjU1kVDWxc1zZ1UNnWwvSY6ZV+SwdisVMbnpDE+N42inDTyM5IHbLk2tXfzwvZa/uv1Azy3rYa0cIgvXzidL35wOukpmt1M4kdKOImf3rCEj/98NV0f+R80tnWRnxH/C4AqgIdQb8SxbncDa3c3kDHnA8yfkMvCiXn9rhRrZiSHjILMFAoyU5ja53OtnT1UNXVQ3dzBweYOthxsZuOB6IKHKaEkcjOSyUkLk5kSJiloURdc+kU+88Ba9je283btYVzwB+DLF87g02dNpjBbcztIfMpJS+bfblzK2Xf+FyverOITZ0wiJRzf5y4UwEOk/nAnK8urqWnpZGZRFs9++ya+8sgrp/Semalhpo/LYvq4LAAiztHY2sXB5g5qWjppbu+msa2bA43tRBz0OkfG7HOpaelkythMrlxQwtnTx3B6ab5GOEhCmFSQQd0f/onwn/9vVpYf5MPzi+P6HIYCeAhsr25hZXk1KaEkrpg/nhnjsnmqpX7I95NkxpisVMZkpXLacZ5z+6Wz2DdKrqOX0alj70bOnT6Wl3bUsW5PI2fE8bkMBfApcM6xYe8hVr1dR3FuGlcuKCYjRV9SkVhbPCmP6uYOVu+spyQvnQl56b5LOinx3YHikXOOF3fUsertOmaMy+KjiycofGX0sCTMzMsNoudOLpw9jpz0ZJ7YfJCO7l7PX5CTo8Q4SS/vrOeNfYdYNCmPD8wYG9f9UCInzEW8DvGE6MrLl88bz+/W7eOp8mquXBB//cFqAZ+EdbsbWL+nkfkTchW+Ih4V5aRx7vSx7KprfWeEUDxRAJ+gzZVNvLyznplFWVwwq1DhK+LZokl5TC7IYNWOOg61dfku54QogE9A5aF2nttaw+SCDC6dO17hKzICmBkXzykilGSsLK8mEkejgBTAg9Ta2cOfNlWRnZbMZfPGJ+z8pCLxKCstzPkzC6lq6uD1vYd8lzNoCuBB6I04/rSpis6eCB+eX0xaDCfLEZGTM3t8NtMKM1m9s56G1vjoilAAD8LqXfVUNnVw8ZwiXcorMkKZGRfMGkc4ZDyztTouFvZUAA/gQGM76/c0Mq8kh1njs32XIyL9yEwNc96MsVQe6uCtymbf5QxIAdyPrp4IK8sPkpuezHkzCn2XIyKDMLc4h4l56bz0dh2tnT2+y+mXArgfL+6opaWjh0vnFsX9rEsio4WZceGccfRGHC9sr/VdTr+UKsexu76VtyqbOX1yPiVxep25yGiVn5HCsrICdtQcZm9Dm+9yjksBfAw9vRGe21pDfkYyZ06N35mWREazJaV55KYn88L2WnojI/OEnAL4GNbubqC5o4cLZ497Z+UKEYkv4VAS580YS0NrF5tG6GXKSpej1B/uZP2eRuaMz2ZifobvckTkFEwdm0lpQQard9XT1jXyTsgpgPtwzvHctlqSQ0mcO2Os73JE5BSZGefPLKSnN8LqnUO/SMKpUgD3saPmMAcOtXPO9LGa21ckQRRkprBwYh6bK5upae7wXc57KIADPb0RVr1dR2FWKqeV5PguR0SG0JlTCkhPDvH89toRdYWcAjjw+r5DtHT0cN6MsSRpljORhJKaHOLs6WOoaupgR81h3+W8QwFMdKaz13Y3MK0wk0kFOvEmkojmFucwJjOFV3bWj5hhaQpgopPt9EYc507XiTeRRJVkxjnTx9LU3s3mypExLG3UB3BDaxfllc0smJhHXkaK73JEJIbKxmQwIS+dNbsa6OqJ+C5HAbx6Vz3hkHFGWb7vUkQkxsyMc6ePpb27lw17G32XM7oDuLq5g7drDrOkNF/DzkRGifG5aUwfl8WGvY3eZ0sb1QG8emc9aeEkFpfm+S5FRIbR2dPG0BNxrN3d4LWOURvA+xvb2NPQxtKyAlLDWmJIZDTJz0hhXkkumw80eV1JeVQGsHOOV3bWk5kaYuHEXN/liIgHZ04pIMmMNRX+WsGjMoAr6lupaurgzLIxhEMJ9iWwJMzMy00knmSmhlk4KY+tB1u8LeI56s48OedYvbOe3PRk5ibiJccuwt0rt3nZ9e2XzvKyX5GTdXppPhv3H2LNrnoun1887PtPsObfwHbWtlJ3uIuzphQQSlKrTWQ0S08JsWhSHttrDlN3uHPY9z+qAti56FnP3PRkZhZphWMRgSWl+aSEknh11/BPVzmqAriivpXalk7OKMsnSa1fEQHSkkMsLs1jZ20rNS3DO13lqAlg5xxrKxrISQsze3wC9v2KyElbXJpHajiJV3cN74iIURPAexvaqG7uZGmZ+n5F5L1SwyGWTM6noq6Vg03D1woeFQHsnGNNRQNZqWHmFKvvV0Teb9HEPNKSk3i1Yvj6gkdFAO9vbKeqqYOlk/O1yrGIHFNKOImlkwvYU99G5aH2YdnnqEijtbsbyEwJaakhEenXgom5pCeHWD1MIyISPoAPHGpnf2M7SybnJ95VbyIypJJDSSwty2d/YzsHhqEVHLNEMrP7zazGzDb32VZgZk+Z2Y7gPuaT8K6taCA9OcT8CZrzQUQGNn9CLhkpIdYMQ19wLJuEDwKXHbXtDuAZ59wM4JngccykFM9kb0MbSybnkazWr4gMQnIoidNL89nX0B7zvuCYpZJz7kXg6EF11wAPBR8/BFwbq/0D5J59PWnhJBZMyIvlbkQkwcwP+oLXxnimtOFuFhY556oAgvtxx3uimd1iZuvMbF1tbe0J72jzgSYypi9jcWk+KWG1fkVk8JJDSSyZnMeehraYjgsescnknLvHObfUObe0sLDwhF//k2d30NtxmIWT1PcrIiduwYQ80pNDMR0XPNwBXG1mxQDBfU0sdtLc0c0b+w7Rsv4xrXYhIiclJViubE997FrBwx3AjwE3BR/fBPwhFjvJSUvmha9dQPOaR2Px9iIySiycmEdaOClma8fFchjaw8BqYJaZ7TezzwLfBy4xsx3AJcHjmEhLDuG6h3dmIxFJLNFWcHSOiOrmoc+TmK2I4Zz75HE+dVGs9ikiMtQWTsplw97GmIyIGLEn4URERoLUcHS+4F11rSSPmzqk760AFhEZwKJJeaSEk8icc96Qvq8CWERkAKnhEJ9aVsqhFx4a+MknQAEsIjIIOenJQ/6eCmAREU8UwCIiniiARUQ8UQCLiHiiABYR8UQBLCLiiQJYRMQTBbCIiCcKYBERTxTAIiKeKIBFRDxRAIuIeKIAFhHxRAEsIuKJAlhExBMFsIiIJwpgERFPFMAiIp4ogEVEPFEAi4h4ogAWEfFEASwi4okCWETEEwWwiIgnCmAREU8UwCIiniiARUQ8UQCLiHiiABYR8UQBLCLiiQJYRMQTBbCIiCcKYBERTxTAIiKeKIBFRDxRAIuIeKIAFhHxRAEsIuKJAlhExBMFsIiIJwpgERFPFMAiIp4ogEVEPFEAi4h4ogAWEfFEASwi4okCWETEEwWwiIgnCmAREU8UwCIiniiARUQ8UQCLiHiiABYR8UQBLCLiiQJYRMQTLwFsZpeZ2TYze9vM7vBRg4iIb8MewGYWAv4VuByYC3zSzOYOdx0iIr75aAEvA952zu1yznUB/wFc46EOERGvzDk3vDs0uw64zDn3ueDxp4EznXNfOup5twC3BA9nAdsGuYuxQN0QlTsSJfrxgY4xEST68cGJHWOdc+6yozeGh7aeQbFjbHvfXwHn3D3APSf85mbrnHNLT6aweJDoxwc6xkSQ6McHQ3OMProg9gOT+jyeCFR6qENExCsfAfwaMMPMpphZCnA98JiHOkREvBr2LgjnXI+ZfQl4EggB9zvn3hrCXZxwt0WcSfTjAx1jIkj044MhOMZhPwknIiJRuhJORMQTBbCIiCcJE8CJeHmzmU0ys+fMbIuZvWVmtwXbC8zsKTPbEdzn+671VJhZyMxeN7MVweNEO748M/u9mW0NvpfLE+kYzewrwc/nZjN72MzS4v34zOx+M6sxs819th33mMzsm0H2bDOzDw12PwkRwAl8eXMP8FXn3BzgLOCvg+O6A3jGOTcDeCZ4HM9uA7b0eZxox/dj4Ann3GxgIdFjTYhjNLMJwK3AUufcPKIn1q8n/o/vQeDoCyeOeUzB7+T1wGnBa34aZNLAnHNxfwOWA0/2efxN4Ju+64rBcf4BuIToVYHFwbZiYJvv2k7hmCYGP8wXAiuCbYl0fDlABcEJ7z7bE+IYgQnAPqCA6KiqFcCliXB8QBmweaDv2dF5Q3SE1/LB7CMhWsC8+0NwxP5gW8IwszJgMbAGKHLOVQEE9+M8lnaqfgR8HYj02ZZIxzcVqAUeCLpZ7jWzTBLkGJ1zB4AfAHuBKqDJObeSBDm+oxzvmE46fxIlgAd1eXO8MrMs4BHgb5xzzb7rGSpmdiVQ45xb77uWGAoDS4CfOecWA63E37/jxxX0g14DTAFKgEwzu8FvVcPupPMnUQI4YS9vNrNkouH7a+fco8HmajMrDj5fDNT4qu8UnQNcbWa7ic6Kd6GZ/YrEOT6I/mzud86tCR7/nmggJ8oxXgxUOOdqnXPdwKPA2STO8fV1vGM66fxJlABOyMubzcyA+4Atzrm7+3zqMeCm4OObiPYNxx3n3DedcxOdc2VEv2fPOuduIEGOD8A5dxDYZ2azgk0XAeUkzjHuBc4ys4zg5/UioicZE+X4+jreMT0GXG9mqWY2BZgBrB3UO/ru6B7CDvMrgO3ATuBbvusZomM6l+i/MhuBN4LbFcAYoieudgT3Bb5rHYJj/SDvnoRLqOMDFgHrgu/jfwH5iXSMwHeArcBm4N+B1Hg/PuBhon3a3URbuJ/t75iAbwXZsw24fLD70aXIIiKeJEoXhIhI3FEAi4h4ogAWEfFEASwi4okCWETEEwWwHJeZfSuY5Wqjmb1hZmfGeH93mtnfBh9/18wuHqL3/Qsz2xQcx2Yzu2Yo3neQ+043sxeOTM5iZk+Y2aEjM7/187pjPi8Y674mmJHrt8G4dyzqn4MZuTaa2ZJge4qZvWhmPhbglQEogOWYzGw5cCWwxDm3gOgVT/v6f9XQcc79L+fc06f6PmY2kegYzXOD4ziL6HjcU3nPwc10FfUXwKPOud7g8V3ApwfxuuM975+AH7rojFyNRMenQnQmwBnB7RbgZwDOuS6iY1Y/cQI1yzBRAMvxFAN1zrlOAOdcnXOuEsDMdpvZP5nZ2uA2PdheaGaPmNlrwe2cYPudwfyqz5vZLjO79chOglb2NjN7GpjVZ/uDZnZdn/19x8w2BC3Z2X3291Sw/RdmtsfMxh51HOOAFuBwcByHnXMVweunm9nTZvZm8B7TgpbkXUFLeZOZfSJ47gctOjfzb4BNFp3D+K7gODea2ReO83X8FH2uAnPOPRPU069jPS+40uxCopczAzwEXBt8fA3wSxf1KpB35LJZohd/fGqgfcrwUwDL8awEJpnZdjP7qZmdf9Tnm51zy4B/ITqjGUTnvf2hc+4M4M+Ae/s8fzbwIWAZ8G0zSzaz04legrwY+ChwRj/11DnnlhBt2f1tsO3bRC9fXgL8J1B6jNe9CVQDFWb2gJld1edzvwb+1Tm3kOj8BVVBHYuIztt7MXBXnyBbRvQqy7lEW55NwbGeAXw+uAz1HUH3wFTn3O5+jutEjAEOOed6gsd9Z93qb0auzfT/tRVPFMByTM65w8DpRP+drQV+a2Y393nKw33ulwcfXwz8i5m9QfT6+Bwzyw4+90fnXKdzro7oJCZFwHnAfzrn2lx0lrf+5u84MhHReqLztEL0Uu3/COp9gui/5EcfRy/RSbKvI3qp+g+DFnk2MME595/B8zqcc23Bez7snOt1zlUDL/BueK090nomOuftjcGxriEajjOO2v1Y4FA/x3Si+pt167ifC74GXX2+FzJCqGNejiv4xX0eeN7MNhGdgOTBI5/u+9TgPonoRNTtfd8n+p8znX029fLuz95gr4U/8vq+rz1W6LyPi15vvxZYa2ZPAQ8Adx/n6f29Z+tRz/uyc+7Jfp7fDqQNVF9wcvMXwcP/5Zw73h+iOqJdC+GgFdx31q2BZuRKBToGqkWGl1rAckxmNsvM+rboFgF7+jz+RJ/71cHHK4Ev9XmPRQPs5kXgI8FIgWzgqgGef7RVwMeDfV1KdJKb9zCzkiMjAgKLgD1Bi3u/mV0bPC/VzDKCmj4R9PEWAh/g2DNbPQn8lUWnC8XMZlp0ovV3OOcagZCZ9RvCzrk1zrlFwe24/wUEf0ieI9qah/fPyHVj0Id9FtHukaqgtjHAkekiZQRRC1iOJwv4iZnlEV2b7m2i3RFHpJrZGqJ/xD8ZbLsV+Fcz20j0Z+tF4C+PtwPn3AYz+y3RWd72AC+dYI3fAR4OTpS9QLQP9+gTXMnAD8yshGgLsLZPTZ8GfmFm3yU669XHiPYlLyfad+yArzvnDh458dfHvUS7QjYEJ8dqefeEWF8riXZrPA1gZi8R7Q/PMrP9wGeP1Yru53nfAP7DzP4eeJ3odKUAfyI6U97bQBvwmT5vd0HweRlhNBuanDCLTqC+NOjP9VlHKtDrnOsJhs39zDm3yGdNRzOzxcDtzrnBDD2LVQ2PEl2zbJuvGuTY1AKWeFYK/M7MkoAu4POe63kf59zrwfC1UJ+xwMMmGInxXwrfkUktYBERT3QSTkTEEwWwiIgnCmAREU8UwCIiniiARUQ8+f9PomDOeKZ0CwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns=['Age', 'Annual Income (k$)','Spending Score (1-100)']\n",
    "for i in columns:   ## For each item/feature in the column variable created,\n",
    "    plt.figure()    ## create a new figure for each feature when\n",
    "    sns.displot(mall_df[i],kde=True);   ## a plot is run"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35334be3",
   "metadata": {},
   "source": [
    "#### However, a stand alone kdeplot allows for a better understanding of the data especially with the additional dimension of gender. In other words. a kdeplot allows for a better understanding /distribution of the data based on gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1c4daa2c",
   "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": [
    "sns.kdeplot(mall_df['Annual Income (k$)'], shade=True,hue=mall_df['Gender']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5b56aea",
   "metadata": {},
   "source": [
    "#### To analyze a kdeplot for all variables, a for loop will be ideal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "182f2093",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns=['Age', 'Annual Income (k$)','Spending Score (1-100)']\n",
    "for i in columns:   ## For each item/feature in the column variable created,\n",
    "    plt.figure()    ## create a new figure for each feature when\n",
    "    sns.kdeplot(mall_df[i], shade=True,hue=mall_df['Gender']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5cbc84b",
   "metadata": {},
   "source": [
    "#### From the above kdeplots, the distribution of each feature for each gender can clearly be identified. Females have more frequency for each feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ea6e2070",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    0.56\n",
       "Male      0.44\n",
       "Name: Gender, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mall_df['Gender'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bd7a49e0",
   "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>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>38.098214</td>\n",
       "      <td>59.250000</td>\n",
       "      <td>51.526786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>39.806818</td>\n",
       "      <td>62.227273</td>\n",
       "      <td>48.511364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age  Annual Income (k$)  Spending Score (1-100)\n",
       "Gender                                                       \n",
       "Female  38.098214           59.250000               51.526786\n",
       "Male    39.806818           62.227273               48.511364"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## To know the mean of each features between the genders\n",
    "mall_df.groupby(['Gender'])['Age', 'Annual Income (k$)','Spending Score (1-100)'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "775d3cb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7XZJVBznH65Jc0/2V8e4k84b1+81WBv/c8i/Aa5McBTwVuHrKtm3Ac6vqNOCPgQun2f8PgU9V1TOB5wN/meSYnmvW3DF/yjDPZUmOBNYC51bVM4D3AX825fl3V9Vzgb8HLgfeBJwCvD7JY7rnvKHbdwz47SntACR5EvAa4Izur429wK/39yvODQ71zCFVdW03rnoecOV+mx8FrEuyHCjgyGkO8SLg5Ul+r1s/CjgJ2NpPxZpj7jfUk+QUBkG+PgnAPGDnlOdf0S2/ClxXVTu7/a4HHgvcxCDsX9E977HA8q59nxXAM4AvdeeYD+x+SH+rOcjgn3uuAP4KOBOY2jt6B/DpqnpF9+awcZp9A7yqqr7Rc41qQxgE+ukH2X5Xt7x3yuN960ckORN4IXB6Ve1JspFBZ2T/c6yrqj94qIpugUM9c8/7gLdX1Vf3a38U933Y+/qD7HsVsDpd1ynJab1UqFZ8A1iY5HSAJEcmecqD2P9RwA+60D8ZePY0z9kAnJvkhO4cC5I87mctfK4z+OeYqvpOVV08zaa/AP48yecZ/Mk9nXcwGAK6NsnXunXpp1JVdwPnAu9M8hVgC/DLD+IQn2DQ87+WwWvxi9Oc4+vAHwGf7J63Hlj8M5Y+5zllgyQ1xh6/JDXG4Jekxhj8ktQYg1+SGmPwS1JjDH41K8miJB9Mcn03H8wXplwl+rMc1xlPNaMZ/GpSd5HavwOfqarHd/PBvBY4cQS1eAW9hsrgV6tewGCSsL/f11BVN1TV2iTzkvxlN0vptUl+C37Sk9+Y5CNJtiX5wJSrnF/ctX0OeOW+Yx5sxtMkr0/yr0n+A/jkUH9zNc+ehlr1FGDzQbadD/ywqp6Z5BHA55PsC+fTun2/C3weOCPJOPAPDN5MJoAPTznWvhlP35Dk0cA1Sf6r23Y68NSquvkh/L2kQzL4JSDJ3wHPAe4GbgCemuTcbvOjGMwKeTdwTVV9p9tnC7AUuB34VlVt79rfD+ybO/5gM54CrDf0NQoGv1p1HfCqfStV9abuVn7jwI3A6qq6auoO3WyRU2eR3Mt9/4cONvfJtDOeJvklBndNk4bOMX616lPAUUneOKXt6G55FfDG7kYiJHnCIW5Isw34hSS/2K2fN2WbM55qxjH41aQazE54DvC8JN9Kcg2wDngr8B7g68DmbpbSd/MAfx1X1Y8YDO38Z/fh7g1TNjvjqWYcZ+eUpMbY45ekxhj8ktQYg1+SGmPwS1JjDH5JaozBL0mNMfglqTH/D2hjnB37WGTFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+WX5akvcOvDJJ0tD109H8buB3gHsAqurrwLEDrEmS1JF+QoGqum2Hpu0DqEWS1LF+OppvS/JsoJI8CngTzaUkSdLM0s+Zwh8CZwCLgU3AbzbLkqQZpp/RR3cDrxpCLZKkjvUz+uiIJJ9LsiXJXUkuSHLEMIqTJA1XP5ePPgacDywCHkfvqWvnDbIoSVI3+uloTlV9eNzyR5L88aAKGrY599/LvJsu6rqMzu31sx8D8It9Dui4ku7Nuf9eYGHXZUid6CcUvpjkTODj9KbMPgn4fJL5AFV17wDrG6hly5Z1XcLI2LjxPgCWHeEfQ1jo74ZmrX5C4aTm9Q07tL+eXkg8YvsXVqxY0XUJI2PlypUArFq1quNKJHWpn9FHhw+jEElS9ybtaE5ydJJfG7f8mmbk0f8Yu3QkSZpZphp99AFgG0CSY4F3Ah+i98Cd1YMvTZI0bFOFwpxxncgnAaur6lNV9R+BaffCJTk0yReTrE9yQ5KVTfv8JJcl2dC8HjjdY0iSpmfKUEgy1udwPHD5uHX9dFBP5kHgP1TVbwDPAs5I8iTgTGBtVR0JrG2WJUlDNNUf9/OALyW5G9gK/G+AJMvYjWc2V9VmYHPz/r4k6+nNq3QCcFyz2RrgCuDN0z2OJGnXTRoKVXVWkrX07mS+tKqqWbUXsEfGciZZChwFXAUsbAKDqtqc5OA9cQxJUv+mvAxUVVdO0PadPXHgJPsDnwL+XVX9OEm/+50OnA6wZMmSPVGKJKnR10N29rQke9MLhI9W1aeb5juTLGrWLwLummjfqlpdVcuravmCBQuGU7AkzRJDD4X0Tgk+CKyvqneNW3UhcGrz/lTggmHXJkmz3e6MIpqu5wD/BvhmkuubtrfSuw/i/CSnAbcCr+igNkma1YYeClX1FWCyDoTjh1mLJOmhOulTkCSNJkNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktSa23UBkkbX2WefzcaNGzutYez4K1eu7LQOgGXLlrFixYquyxgoQ0HSSJs3b17XJcwqhoKkSc30b8V6OPsUJEktQ0GS1DIUJEktQ0GS1DIUJEmtkRt9lOSFwCpgDnBOVb2z45IGzrHgDzUbxoJLo2qkzhSSzAHeA7wIeBJwSpIndVvV7DBv3jzHg0sauTOFZwIbq+pmgCQfB04Abuy0qgHzW7GkUTFSZwrAYuC2ccu3N22tJKcnWZdk3ZYtW4ZanCTNdKMWCpmgrR6yULW6qpZX1fIFCxYMqSxJmh1GLRRuBw4dt3wIcEdHtUjSrDNqofA14Mgkhyd5FHAycGHHNUnSrDFSHc1V9WCSPwYuoTck9dyquqHjsiRp1hipUACoqouAi7quQ5Jmo1G7fCRJ6pChIElqpap2vtWISrIFuKXrOmaQg4C7uy5CmoC/m3vWYVU14Zj+R3QoaM9Ksq6qlnddh7QjfzeHx8tHkqSWoSBJahkKGm911wVIk/B3c0jsU5AktTxTkCS1DAVJUstQmOGSVJIPj1uem2RLkn/ayX7H7WwbqR9Jtie5ftzP0gEe63tJDhrU588GIzf3kfa4nwJPSTKvqrYCLwA2dVyTZpetVfWbXReh/nimMDt8Afjd5v0pwHljK5I8M8n/SXJd8/qEHXdOsl+Sc5N8rdnuhCHVrRkqyTOSfCnJNUkuSbKoab8iyd8l+XKS9UmOTvLpJBuS/Jdx+3+22feGJKdPcoxXJ7m6OTv5QPMMeO2EoTA7fBw4Ock+wFOBq8atuwk4tqqOAv4T8I4J9v8L4PKqOhr4l8DfJNlvwDVr5pg37tLRZ5LsDZwNnFhVzwDOBc4at/22qjoWeD9wAXAG8BTgtUke22zz+mbf5cCbxrUDkOQ3gJOA5zRnKduBVw3unzhzePloFqiqbzTXcU/h4dOSPwZYk+RIeo8+3XuCj/hXwEuS/GmzvA+wBFg/mIo1wzzk8lGSp9D7I39ZEug9O2XzuO3HHqz1TeCGqtrc7HczvScz3kMvCF7WbHcocGTTPuZ44BnA15pjzAPu2qP/qhnKUJg9LgT+FjgOGP+t6q+BL1bVy5rguGKCfQP8QVV9e8A1anYIvT/2x0yy/oHm9Rfj3o8tz01yHPB84Jiquj/JFfS+qOx4jDVV9ZY9VfRs4eWj2eNc4K+q6ps7tD+GX3Y8v3aSfS8BVqT5ypXkqIFUqNni28CCJMcAJNk7yZN3Yf/HAD9oAuGJwLMm2GYtcGKSg5tjzE9y2O4WPhsYCrNEVd1eVasmWPXfgf+a5Kv0TuMn8tf0Lit9I8m3mmVpWqpqG3Ai8N+SfB24Hnj2LnzExfTOGL5B73fxygmOcSPwl8ClzXaXAYt2s/RZwWkuJEktzxQkSS1DQZLUMhQkSS1DQZLUMhQkSS1DQZpAkoVJPpbk5maOnf877g7a3flcZ5/VSDMUpB00N+l9FvhyVR3RzLFzMnBIB7U464CGylCQHu559CZle/9YQ1XdUlVnJ5mT5G+aGWO/keQN0J4BXJHkk0luSvLRcXeAv7Bp+wrw8rHPnGz22SSvTfKJJJ8DLh3qv1yznt9CpId7MnDtJOtOA35UVUcneTTw1SRjf7iPava9A/gq8Jwk64C/pxc0G4H/Ne6zxmaffX2SXwWuTvLPzbpjgKdW1b178N8l7ZShIO1EkvcAzwW2AbcAT01yYrP6MfRm6NwGXF1Vtzf7XA8sBX4CfLeqNjTtHwHG5v+fbPZZgMsMBHXBUJAe7gbgD8YWquqM5hGP64BbgRVVdcn4HZqZO8fP6LmdX/7/NdlcMhPOPpvkX9B7Yp40dPYpSA93ObBPkj8a17Zv83oJ8EfNg2JI8us7eeDQTcDhSR7fLJ8ybp2zz2rkGArSDqo3S+RLgd9O8t0kVwNrgDcD5wA3Atc2M8Z+gCnOuKvqZ/QuF32+6Wi+ZdxqZ5/VyHGWVElSyzMFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLr/wOHJSVyQx3WtQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### A boxplot can be used to dive deeper to check for outliers and gives an idea of the percentiles that the data is concentrated between\n",
    "columns=['Age', 'Annual Income (k$)','Spending Score (1-100)']\n",
    "for i in columns:   ## For each item/feature in the column variable created,\n",
    "    plt.figure()    ## create a new figure for each feature when\n",
    "    sns.boxplot(data=mall_df, x='Gender',y=mall_df[i]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56c463d9",
   "metadata": {},
   "source": [
    "# BIVARIATE ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "03fd55ae",
   "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": [
    "sns.scatterplot(data=mall_df, x='Annual Income (k$)', y= 'Spending Score (1-100)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "611601df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1f259613520>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792.125x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### To view the relationship amongst the variables, a pairplot is best used\n",
    "mall_df.drop('CustomerID',axis=1)\n",
    "sns.pairplot(mall_df,hue='Gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "da2aec10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.026763</td>\n",
       "      <td>0.977548</td>\n",
       "      <td>0.013835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>-0.026763</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012398</td>\n",
       "      <td>-0.327227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <td>0.977548</td>\n",
       "      <td>-0.012398</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.009903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "      <td>0.013835</td>\n",
       "      <td>-0.327227</td>\n",
       "      <td>0.009903</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        CustomerID       Age  Annual Income (k$)  \\\n",
       "CustomerID                1.000000 -0.026763            0.977548   \n",
       "Age                      -0.026763  1.000000           -0.012398   \n",
       "Annual Income (k$)        0.977548 -0.012398            1.000000   \n",
       "Spending Score (1-100)    0.013835 -0.327227            0.009903   \n",
       "\n",
       "                        Spending Score (1-100)  \n",
       "CustomerID                            0.013835  \n",
       "Age                                  -0.327227  \n",
       "Annual Income (k$)                    0.009903  \n",
       "Spending Score (1-100)                1.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## To find the degree of the relationship amongst the variables, a correlation funstion is used\n",
    "mall_df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e5868979",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## A heatmap is used to visualize the degree of correlation\n",
    "sns.heatmap(mall_df.corr(), annot=True,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d2ca56e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CustomerID', 'Gender', 'Age', 'Annual Income (k$)',\n",
       "       'Spending Score (1-100)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mall_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db2e40ba",
   "metadata": {},
   "source": [
    "# CLUSTERING - UNIVARIATE, BIVARIATE AND MULTIVARIATE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd382355",
   "metadata": {},
   "source": [
    "##### For any machine learning algorithm in sklearn to be done, there are three steps\n",
    "##### The first thing is to initialize  the algorithm\n",
    "##### The second is to fit our data to that algorithm which allows the algorithm to learn the data\n",
    "##### The third is to either predict or gather the necessary labels needed out of that fitted model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a170def9",
   "metadata": {},
   "source": [
    "## UNIVARIATE CLUSTERING (Annual Income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bb720b4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Here we perform the first step by initializing the KMeans algorithm\n",
    "clustering1 = KMeans() ## The standard parameters for clusters in this algorithm is 8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5393d82c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Here we fit the feature that we want the algorith,KMeans, to learn\n",
    "clustering1.fit(mall_df[['Annual Income (k$)']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a54cb7f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 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, 6, 6, 6, 6, 6, 6, 6, 6,\n",
       "       6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 3, 3, 3, 3,\n",
       "       3, 3])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### A lst of the labels. which we already know are 8 since the default cluster is 8\n",
    "clustering1.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ee9a83a3",
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "      <th>Income Cluster</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>4</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>4</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>3</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>3</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>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     CustomerID  Gender  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",
       "     Income Cluster  \n",
       "0                 4  \n",
       "1                 4  \n",
       "2                 4  \n",
       "3                 4  \n",
       "4                 4  \n",
       "..              ...  \n",
       "195               3  \n",
       "196               3  \n",
       "197               3  \n",
       "198               3  \n",
       "199               3  \n",
       "\n",
       "[200 rows x 6 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Now we need to compare these labels to our initial data so we can make meaning out of it\n",
    "mall_df['Income Cluster']=clustering1.labels_\n",
    "mall_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "37921568",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    42\n",
       "2    36\n",
       "4    30\n",
       "1    28\n",
       "6    28\n",
       "0    16\n",
       "7    14\n",
       "3     6\n",
       "Name: Income Cluster, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Now we can do some summary statistics on the clusters\n",
    "# 1. the number of customers in each cluster\n",
    "mall_df['Income Cluster'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60ebe363",
   "metadata": {},
   "source": [
    "##   ELBOW METHOD\n",
    "#### Rather than using a default clustering number, the elbow method can help us in identifying the optimal number of clusters to choose. This can be done by selecting the right inertia(WCSS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0862c628",
   "metadata": {},
   "outputs": [],
   "source": [
    "## To efficient way to check the inertia(WCSS) on ach cluster is to create a for loop\n",
    "inertia_scores=[]    ## create an empty list to put all the inertia scores in once calculated\n",
    "for i in range(1,11):   ## For each cluster in a range of 1 to 10,\n",
    "    kmeans=KMeans(n_clusters=i) ### Initialize the algorithm\n",
    "    kmeans.fit(mall_df[['Annual Income (k$)']])  ## Fit the data to be studied by the algorithm\n",
    "    inertia_scores.append(kmeans.inertia_)     ### append the calculated WCSS to inertia scores that was created\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "35fb8d7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[137277.28000000003,\n",
       " 48660.88888888889,\n",
       " 23517.330930930926,\n",
       " 13278.112713472487,\n",
       " 8481.496190476191,\n",
       " 5050.904761904763,\n",
       " 3941.4163614163617,\n",
       " 2831.2960317460324,\n",
       " 2173.287445887446,\n",
       " 1780.430554412908]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inertia_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f490c562",
   "metadata": {
    "scrolled": true
   },
   "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": [
    "### We need to pair the inertia scores with their respective clusters\n",
    "plt.plot(range(1,11), inertia_scores, marker='o')\n",
    "plt.title('The Elbow Method')\n",
    "plt.ylabel(\"WCSS\")\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cb32e06",
   "metadata": {},
   "source": [
    "### KNEELOCATOR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4ed4ead7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: kneed in c:\\users\\gertr\\anaconda3\\lib\\site-packages (0.8.1)\n",
      "Requirement already satisfied: scipy>=1.0.0 in c:\\users\\gertr\\anaconda3\\lib\\site-packages (from kneed) (1.7.3)\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Requirement already satisfied: numpy>=1.14.2 in c:\\users\\gertr\\anaconda3\\lib\\site-packages (from kneed) (1.21.5)\n"
     ]
    }
   ],
   "source": [
    "pip install kneed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "12be2033",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### In case we are unsure about the exact number from the elbow method, the KneeLocater function from the Kneed Library can help.\n",
    "from kneed import KneeLocator\n",
    "\n",
    "k1=KneeLocator(range(1,11), inertia_scores, curve='convex', direction='decreasing')\n",
    "k1.elbow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "eed34d6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 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])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## With our chosen cluster, we can input it into the KMeans algorithm\n",
    "kmeans1=KMeans(n_clusters=3) ## initialize algorithm with optimal clusters\n",
    "kmeans1.fit(mall_df[['Annual Income (k$)']])\n",
    "kmeans1.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d9c58f66",
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "      <th>Income Cluster</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>0</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>0</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>0</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>0</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>0</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>1</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>1</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  Gender  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",
       "     Income Cluster  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 0  \n",
       "4                 0  \n",
       "..              ...  \n",
       "195               1  \n",
       "196               1  \n",
       "197               1  \n",
       "198               1  \n",
       "199               1  \n",
       "\n",
       "[200 rows x 6 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mall_df['Income Cluster']=kmeans1.labels_\n",
    "mall_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "87a950f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    90\n",
       "0    74\n",
       "1    36\n",
       "Name: Income Cluster, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mall_df['Income Cluster'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "53e8bf6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CustomerID', 'Gender', 'Age', 'Annual Income (k$)',\n",
       "       'Spending Score (1-100)', 'Income Cluster'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mall_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c65b6b1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39.500000</td>\n",
       "      <td>33.486486</td>\n",
       "      <td>50.229730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37.833333</td>\n",
       "      <td>99.888889</td>\n",
       "      <td>50.638889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38.722222</td>\n",
       "      <td>67.088889</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Age  Annual Income (k$)  Spending Score (1-100)\n",
       "Income Cluster                                                       \n",
       "0               39.500000           33.486486               50.229730\n",
       "1               37.833333           99.888889               50.638889\n",
       "2               38.722222           67.088889               50.000000"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## We can now check the mean for each feature in each cluster\n",
    "mall_df.groupby('Income Cluster')['Age', 'Annual Income (k$)',\n",
    "       'Spending Score (1-100)'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e33380f1",
   "metadata": {},
   "source": [
    "## BIVARIATE CLUSTERING (Annual Income & Spending Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bbaa10d8",
   "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": [
    "inertia_scores2=[]    ## create an empty list to put all the inertia scores in once calculated\n",
    "for i in range(1,11):   ## For each cluster in a range of 1 to 10,\n",
    "    kmeans2=KMeans(n_clusters=i) ### Initialize the algorithm\n",
    "    kmeans2.fit(mall_df[['Annual Income (k$)','Spending Score (1-100)']])  ## Fit the data to be studied by the algorithm\n",
    "    inertia_scores2.append(kmeans2.inertia_)     ### append the calculated WCSS to inertia scores that was created\n",
    "    \n",
    "plt.plot(range(1,11), inertia_scores2, marker='o')\n",
    "plt.title('The Elbow Method')\n",
    "plt.ylabel(\"WCSS\")\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "739cf97e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k2=KneeLocator(range(1,11), inertia_scores2, curve='convex', direction='decreasing')\n",
    "k2.elbow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6fd7088b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "      <th>Income Cluster</th>\n",
       "      <th>Income &amp; Spending Cluster</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>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>4</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>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>4</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>0</td>\n",
       "      <td>1</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",
       "      <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",
       "      <td>0</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>1</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",
       "      <td>0</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>1</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     CustomerID  Gender  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",
       "     Income Cluster  Income & Spending Cluster  \n",
       "0                 0                          1  \n",
       "1                 0                          4  \n",
       "2                 0                          1  \n",
       "3                 0                          4  \n",
       "4                 0                          1  \n",
       "..              ...                        ...  \n",
       "195               1                          0  \n",
       "196               1                          3  \n",
       "197               1                          0  \n",
       "198               1                          3  \n",
       "199               1                          0  \n",
       "\n",
       "[200 rows x 7 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## With our chosen cluster, we can input it into the KMeans algorithm\n",
    "kmeans2=KMeans(n_clusters=5) ## initialize algorithm with optimal clusters\n",
    "kmeans2.fit(mall_df[['Annual Income (k$)','Spending Score (1-100)']])\n",
    "mall_df['Income & Spending Cluster']=kmeans2.labels_\n",
    "mall_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3c0550a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[86.53846154, 82.12820513],\n",
       "       [26.30434783, 20.91304348],\n",
       "       [55.2962963 , 49.51851852],\n",
       "       [88.2       , 17.11428571],\n",
       "       [25.72727273, 79.36363636]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## We can get the centriods for each cluster with the 'cluster_centers_'function. The results are the x and y cordinates\n",
    "kmeans2.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7b5db15f",
   "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>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86.538462</td>\n",
       "      <td>82.128205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26.304348</td>\n",
       "      <td>20.913043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55.296296</td>\n",
       "      <td>49.518519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>88.200000</td>\n",
       "      <td>17.114286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25.727273</td>\n",
       "      <td>79.363636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           x          y\n",
       "0  86.538462  82.128205\n",
       "1  26.304348  20.913043\n",
       "2  55.296296  49.518519\n",
       "3  88.200000  17.114286\n",
       "4  25.727273  79.363636"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## In order to add the centriods into a scatter plot, we first make it a dataframe\n",
    "centers=pd.DataFrame(kmeans2.cluster_centers_)\n",
    "centers.columns=['x','y']\n",
    "centers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e72de2ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Annual Income (k$)', ylabel='Spending Score (1-100)'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Visualize to better understand clustering result\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.scatter(x=centers['x'],y=centers['y'], s=100, c='black',marker='*')\n",
    "sns.scatterplot(data=mall_df, x='Annual Income (k$)',y='Spending Score (1-100)',hue='Income & Spending Cluster',palette='tab10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "89ea33b9",
   "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>Gender</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income &amp; Spending Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.461538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.608696</td>\n",
       "      <td>0.391304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.592593</td>\n",
       "      <td>0.407407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.457143</td>\n",
       "      <td>0.542857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.409091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Gender                       Female      Male\n",
       "Income & Spending Cluster                    \n",
       "0                          0.538462  0.461538\n",
       "1                          0.608696  0.391304\n",
       "2                          0.592593  0.407407\n",
       "3                          0.457143  0.542857\n",
       "4                          0.590909  0.409091"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## For further analysis, we can use a crosstab to breakdown results into male and female\n",
    "pd.crosstab(mall_df['Income & Spending Cluster'],mall_df['Gender'],normalize='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4811c865",
   "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>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income &amp; Spending Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.692308</td>\n",
       "      <td>86.538462</td>\n",
       "      <td>82.128205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45.217391</td>\n",
       "      <td>26.304348</td>\n",
       "      <td>20.913043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42.716049</td>\n",
       "      <td>55.296296</td>\n",
       "      <td>49.518519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41.114286</td>\n",
       "      <td>88.200000</td>\n",
       "      <td>17.114286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25.272727</td>\n",
       "      <td>25.727273</td>\n",
       "      <td>79.363636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Age  Annual Income (k$)  \\\n",
       "Income & Spending Cluster                                  \n",
       "0                          32.692308           86.538462   \n",
       "1                          45.217391           26.304348   \n",
       "2                          42.716049           55.296296   \n",
       "3                          41.114286           88.200000   \n",
       "4                          25.272727           25.727273   \n",
       "\n",
       "                           Spending Score (1-100)  \n",
       "Income & Spending Cluster                          \n",
       "0                                       82.128205  \n",
       "1                                       20.913043  \n",
       "2                                       49.518519  \n",
       "3                                       17.114286  \n",
       "4                                       79.363636  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mall_df.groupby('Income & Spending Cluster')['Age', 'Annual Income (k$)','Spending Score (1-100)'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba6891c5",
   "metadata": {},
   "source": [
    "#### From the above, we can say that the for our target group which is the orange cluster(cluster 1) are those with high annual income and high spending score. And this cluster has a higher percentage of females(53%).We can also say that in this cluster,the average age is 32, average income is 86 thousand and average spending score is 82"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8d1c8a1",
   "metadata": {},
   "source": [
    "## MULTIVARIATE CLUSTERING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "80720404",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Annual Income (k$)</th>\n",
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      ],
      "text/plain": [
       "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)  \\\n",
       "0           1    Male   19                  15                      39   \n",
       "1           2    Male   21                  15                      81   \n",
       "2           3  Female   20                  16                       6   \n",
       "3           4  Female   23                  16                      77   \n",
       "4           5  Female   31                  17                      40   \n",
       "\n",
       "   Income Cluster  Income & Spending Cluster  \n",
       "0               0                          1  \n",
       "1               0                          4  \n",
       "2               0                          1  \n",
       "3               0                          4  \n",
       "4               0                          1  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## From the data frame, we would need to encode Gender by replacing them with numbers. This allows to easily feed it into the ML algorithm\n",
    "mall_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3ba77db1",
   "metadata": {},
   "outputs": [
    {
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       "<p>200 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     CustomerID  Age  Annual Income (k$)  Spending Score (1-100)  \\\n",
       "0             1   19                  15                      39   \n",
       "1             2   21                  15                      81   \n",
       "2             3   20                  16                       6   \n",
       "3             4   23                  16                      77   \n",
       "4             5   31                  17                      40   \n",
       "..          ...  ...                 ...                     ...   \n",
       "195         196   35                 120                      79   \n",
       "196         197   45                 126                      28   \n",
       "197         198   32                 126                      74   \n",
       "198         199   32                 137                      18   \n",
       "199         200   30                 137                      83   \n",
       "\n",
       "     Income Cluster  Income & Spending Cluster  Gender_Female  Gender_Male  \n",
       "0                 0                          1              0            1  \n",
       "1                 0                          4              0            1  \n",
       "2                 0                          1              1            0  \n",
       "3                 0                          4              1            0  \n",
       "4                 0                          1              1            0  \n",
       "..              ...                        ...            ...          ...  \n",
       "195               1                          0              1            0  \n",
       "196               1                          3              1            0  \n",
       "197               1                          0              0            1  \n",
       "198               1                          3              0            1  \n",
       "199               1                          0              0            1  \n",
       "\n",
       "[200 rows x 8 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## replace the Gender feature with dummy variables through one hot encoding\n",
    "mall_df2 = pd.get_dummies(mall_df)\n",
    "mall_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "360bacb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "     CustomerID  Age  Annual Income (k$)  Spending Score (1-100)  \\\n",
       "0             1   19                  15                      39   \n",
       "1             2   21                  15                      81   \n",
       "2             3   20                  16                       6   \n",
       "3             4   23                  16                      77   \n",
       "4             5   31                  17                      40   \n",
       "..          ...  ...                 ...                     ...   \n",
       "195         196   35                 120                      79   \n",
       "196         197   45                 126                      28   \n",
       "197         198   32                 126                      74   \n",
       "198         199   32                 137                      18   \n",
       "199         200   30                 137                      83   \n",
       "\n",
       "     Income Cluster  Income & Spending Cluster  Gender_Male  \n",
       "0                 0                          1            1  \n",
       "1                 0                          4            1  \n",
       "2                 0                          1            0  \n",
       "3                 0                          4            0  \n",
       "4                 0                          1            0  \n",
       "..              ...                        ...          ...  \n",
       "195               1                          0            0  \n",
       "196               1                          3            0  \n",
       "197               1                          0            1  \n",
       "198               1                          3            1  \n",
       "199               1                          0            1  \n",
       "\n",
       "[200 rows x 7 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### One of the dummy variables can be dropped since we can infer if its male or female from only one dummy\n",
    "mall_df2 = pd.get_dummies(mall_df,drop_first=True)\n",
    "mall_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6581e9f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CustomerID', 'Age', 'Annual Income (k$)', 'Spending Score (1-100)',\n",
       "       'Income Cluster', 'Income & Spending Cluster', 'Gender_Male'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mall_df2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "cb5aeb49",
   "metadata": {},
   "outputs": [
    {
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       "      <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 × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Annual Income (k$)  Spending Score (1-100)  Gender_Male\n",
       "0     19                  15                      39            1\n",
       "1     21                  15                      81            1\n",
       "2     20                  16                       6            0\n",
       "3     23                  16                      77            0\n",
       "4     31                  17                      40            0\n",
       "..   ...                 ...                     ...          ...\n",
       "195   35                 120                      79            0\n",
       "196   45                 126                      28            0\n",
       "197   32                 126                      74            1\n",
       "198   32                 137                      18            1\n",
       "199   30                 137                      83            1\n",
       "\n",
       "[200 rows x 4 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mall_df2=mall_df2[['Age', 'Annual Income (k$)', 'Spending Score (1-100)','Gender_Male']]\n",
    "mall_df2               "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "758cf65a",
   "metadata": {},
   "outputs": [],
   "source": [
    "### the variables need to be standardized so that the algorithm puts them on the same scale\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c83b8c1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "scale=StandardScaler()   ### Initialize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b3dce557",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.424569</td>\n",
       "      <td>-1.738999</td>\n",
       "      <td>-0.434801</td>\n",
       "      <td>1.128152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.281035</td>\n",
       "      <td>-1.738999</td>\n",
       "      <td>1.195704</td>\n",
       "      <td>1.128152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.352802</td>\n",
       "      <td>-1.700830</td>\n",
       "      <td>-1.715913</td>\n",
       "      <td>-0.886405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.137502</td>\n",
       "      <td>-1.700830</td>\n",
       "      <td>1.040418</td>\n",
       "      <td>-0.886405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.563369</td>\n",
       "      <td>-1.662660</td>\n",
       "      <td>-0.395980</td>\n",
       "      <td>-0.886405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0 -1.424569 -1.738999 -0.434801  1.128152\n",
       "1 -1.281035 -1.738999  1.195704  1.128152\n",
       "2 -1.352802 -1.700830 -1.715913 -0.886405\n",
       "3 -1.137502 -1.700830  1.040418 -0.886405\n",
       "4 -0.563369 -1.662660 -0.395980 -0.886405"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mall_df2 = pd.DataFrame(scale.fit_transform(mall_df2))    ## fit the data to be studied by the algorithm\n",
    "mall_df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "61bcf5aa",
   "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": [
    "## Now we can go through the clustering process for multiple features\n",
    "inertia_scores3=[]    ## create an empty list to put all the inertia scores in once calculated\n",
    "for i in range(1,11):   ## For each cluster in a range of 1 to 10,\n",
    "    kmeans3=KMeans(n_clusters=i) ### Initialize the algorithm\n",
    "    kmeans3.fit(mall_df2)  ## Fit the data to be studied by the algorithm\n",
    "    inertia_scores3.append(kmeans3.inertia_)     ### append the calculated WCSS to inertia scores that was created\n",
    "    \n",
    "plt.plot(range(1,11), inertia_scores3, marker='o')\n",
    "plt.title('The Elbow Method')\n",
    "plt.ylabel(\"WCSS\")\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e8a790c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>Cluster Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.424569</td>\n",
       "      <td>-1.738999</td>\n",
       "      <td>-0.434801</td>\n",
       "      <td>1.128152</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.281035</td>\n",
       "      <td>-1.738999</td>\n",
       "      <td>1.195704</td>\n",
       "      <td>1.128152</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.352802</td>\n",
       "      <td>-1.700830</td>\n",
       "      <td>-1.715913</td>\n",
       "      <td>-0.886405</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.137502</td>\n",
       "      <td>-1.700830</td>\n",
       "      <td>1.040418</td>\n",
       "      <td>-0.886405</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.563369</td>\n",
       "      <td>-1.662660</td>\n",
       "      <td>-0.395980</td>\n",
       "      <td>-0.886405</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>-0.276302</td>\n",
       "      <td>2.268791</td>\n",
       "      <td>1.118061</td>\n",
       "      <td>-0.886405</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>0.441365</td>\n",
       "      <td>2.497807</td>\n",
       "      <td>-0.861839</td>\n",
       "      <td>-0.886405</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>-0.491602</td>\n",
       "      <td>2.497807</td>\n",
       "      <td>0.923953</td>\n",
       "      <td>1.128152</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>-0.491602</td>\n",
       "      <td>2.917671</td>\n",
       "      <td>-1.250054</td>\n",
       "      <td>1.128152</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>-0.635135</td>\n",
       "      <td>2.917671</td>\n",
       "      <td>1.273347</td>\n",
       "      <td>1.128152</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3  Cluster Label\n",
       "0   -1.424569 -1.738999 -0.434801  1.128152              2\n",
       "1   -1.281035 -1.738999  1.195704  1.128152              2\n",
       "2   -1.352802 -1.700830 -1.715913 -0.886405              0\n",
       "3   -1.137502 -1.700830  1.040418 -0.886405              1\n",
       "4   -0.563369 -1.662660 -0.395980 -0.886405              1\n",
       "..        ...       ...       ...       ...            ...\n",
       "195 -0.276302  2.268791  1.118061 -0.886405              1\n",
       "196  0.441365  2.497807 -0.861839 -0.886405              0\n",
       "197 -0.491602  2.497807  0.923953  1.128152              2\n",
       "198 -0.491602  2.917671 -1.250054  1.128152              3\n",
       "199 -0.635135  2.917671  1.273347  1.128152              2\n",
       "\n",
       "[200 rows x 5 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## With our chosen cluster, we can input it into the KMeans algorithm\n",
    "kmeans3=KMeans(n_clusters=4) ## initialize algorithm with optimal clusters\n",
    "kmeans3.fit(mall_df2)\n",
    "mall_df2['Cluster Label']=kmeans3.labels_\n",
    "mall_df2"
   ]
  }
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