06_Customer_Segmentation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns #visualisation\n",
    "import matplotlib.pyplot as plt #visualisation\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder, LabelEncoder \n",
    "from sklearn.metrics import silhouette_score\n",
    "%matplotlib inline \n",
    "sns.set(color_codes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Data/Mall_Customers.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Gender  Age  Annual Income (k$)  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"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID                 int64\n",
       "Gender                    object\n",
       "Age                        int64\n",
       "Annual Income (k$)         int64\n",
       "Spending Score (1-100)     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 5)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID                200\n",
       "Gender                    200\n",
       "Age                       200\n",
       "Annual Income (k$)        200\n",
       "Spending Score (1-100)    200\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 5)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   </g>\r\n",
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       "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\" y=\"-10\"/>\r\n",
       "   <g id=\"patch_4\">\r\n",
       "    <path d=\"M 976.214063 554.736797 \r\n",
       "L 976.214063 552.613359 \r\n",
       "L 976.214063 13.260234 \r\n",
       "L 976.214063 11.136797 \r\n",
       "L 1003.394063 11.136797 \r\n",
       "L 1003.394063 13.260234 \r\n",
       "L 1003.394063 552.613359 \r\n",
       "L 1003.394063 554.736797 \r\n",
       "z\r\n",
       "\" style=\"fill:none;\"/>\r\n",
       "   </g>\r\n",
       "  </g>\r\n",
       " </g>\r\n",
       " <defs>\r\n",
       "  <clipPath id=\"pb9fc62f11b\">\r\n",
       "   <rect height=\"543.6\" width=\"892.8\" x=\"27.614063\" y=\"11.136797\"/>\r\n",
       "  </clipPath>\r\n",
       "  <clipPath id=\"pf2206b4eef\">\r\n",
       "   <rect height=\"543.6\" width=\"27.18\" x=\"976.214063\" y=\"11.136797\"/>\r\n",
       "  </clipPath>\r\n",
       " </defs>\r\n",
       "</svg>\r\n"
      ],
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "c= df.corr()\n",
    "sns.heatmap(c,cmap=\"BrBG\",annot=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
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     },
     "metadata": {},
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   ],
   "source": [
    "df.Age.value_counts().nlargest(40).plot(kind=\"bar\", figsize=(10,5))\n",
    "plt.title(\"Histogram to Show Count of Age Class\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.xlabel(\"Age Class\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "sns.boxplot(x=df[\"Age\"]);"
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  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gender'] = LabelEncoder().fit_transform(df['Gender'])"
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  {
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   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "Clus_dataSet = StandardScaler().fit_transform(df)"
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  {
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   "metadata": {},
   "outputs": [],
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    "kmeans_per_k = [KMeans(n_clusters=k, random_state=42).fit(Clus_dataSet)\n",
    "                for k in range(1, 10)]\n",
    "inertias = [model.inertia_ for model in kmeans_per_k]"
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   "execution_count": 16,
   "metadata": {},
   "outputs": [
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     "data": {
      "text/plain": [
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     "execution_count": 16,
     "metadata": {},
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      ],
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(1,10),inertias,color='blue', marker='o',\n",
    "         markerfacecolor='red', markersize=10)\n",
    "#plt.axis([1, 8.5, 0, 1300])\n",
    "plt.title('Inertia vs. K Value')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Inertia')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "silhouette_scores = [silhouette_score(Clus_dataSet, model.labels_)\n",
    "                     for model in kmeans_per_k[1:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 3))\n",
    "plt.plot(range(2, 10), silhouette_scores, \"bo-\")\n",
    "plt.xlabel(\"$k$\", fontsize=14)\n",
    "plt.ylabel(\"Silhouette score\", fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(\n",
    "                n_clusters=5, \n",
    "                init='k-means++', \n",
    "                n_init=10,\n",
    "                max_iter=300, \n",
    "                tol=0.0001, \n",
    "                precompute_distances='auto', \n",
    "                verbose=0, \n",
    "                random_state=101, \n",
    "                copy_x=True, \n",
    "                n_jobs=-1, \n",
    "                algorithm='auto'\n",
    "                )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=5, n_jobs=-1, precompute_distances='auto', random_state=101)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.fit(Clus_dataSet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1 1 2 1 4 1 4 1 2 1 4 1 1 1 4 1 1 1 2 1 2 1 2 1 2 1 4 1 4 1 2 1 2\n",
      " 1 2 1 2 1 4 1 2 1 2 1 1 1 2 1 1 4 2 4 2 4 1 4 4 1 2 2 4 1 2 2 1 1 4 2 2 2\n",
      " 4 1 2 4 1 2 4 4 4 2 1 4 2 1 2 2 2 1 4 2 2 1 2 1 4 1 1 2 4 1 4 1 2 4 4 4 4\n",
      " 1 2 1 1 1 2 2 2 2 3 2 2 3 0 3 0 3 4 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3\n",
      " 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0\n",
      " 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3]\n"
     ]
    }
   ],
   "source": [
    "k_means_labels = kmeans.labels_\n",
    "print(k_means_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.08067278,  0.14809719,  0.07477313,  1.02570541, -1.21752808],\n",
       "       [-0.87175534, -0.0881844 , -1.00209318, -0.84165752,  0.41268052],\n",
       "       [-0.4999775 , -0.88640526,  0.76168963, -0.44961726, -0.33159294],\n",
       "       [ 1.04747082,  0.07050951, -0.45213014,  0.97293875,  1.21414431],\n",
       "       [-0.52350397,  1.12815215,  1.28276771, -0.47677566, -0.40668932]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_means_cluster_centers = kmeans.cluster_centers_\n",
    "k_means_cluster_centers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Clus_km\"] = kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
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       "      <th>CustomerID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
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       "      <th>Clus_km</th>\n",
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      "text/plain": [
       "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)  \\\n",
       "0           1       1   19                  15                      39   \n",
       "1           2       1   21                  15                      81   \n",
       "2           3       0   20                  16                       6   \n",
       "3           4       0   23                  16                      77   \n",
       "4           5       0   31                  17                      40   \n",
       "\n",
       "   Clus_km  \n",
       "0        1  \n",
       "1        1  \n",
       "2        1  \n",
       "3        1  \n",
       "4        1  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Gender</th>\n",
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       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clus_km</th>\n",
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       "      <td>32.550000</td>\n",
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       "      <td>81.475000</td>\n",
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       "      <td>70.275862</td>\n",
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      "text/plain": [
       "         CustomerID    Gender        Age  Annual Income (k$)  \\\n",
       "Clus_km                                                        \n",
       "0        162.891892  0.513514  39.891892           87.432432   \n",
       "1         50.169811  0.396226  24.886792           38.509434   \n",
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       "4         70.275862  1.000000  56.724138           48.068966   \n",
       "\n",
       "         Spending Score (1-100)  \n",
       "Clus_km                          \n",
       "0                     18.837838  \n",
       "1                     60.830189  \n",
       "2                     41.658537  \n",
       "3                     81.475000  \n",
       "4                     39.724138  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.groupby('Clus_km').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    53\n",
       "2    41\n",
       "3    40\n",
       "0    37\n",
       "4    29\n",
       "Name: Clus_km, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Clus_km\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>44</td>\n",
       "      <td>46</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>46</td>\n",
       "      <td>56</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>48</td>\n",
       "      <td>51</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>49</td>\n",
       "      <td>55</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>54</td>\n",
       "      <td>47</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>54</td>\n",
       "      <td>48</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>54</td>\n",
       "      <td>51</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>54</td>\n",
       "      <td>55</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>83</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>54</td>\n",
       "      <td>41</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>54</td>\n",
       "      <td>46</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>93</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>60</td>\n",
       "      <td>49</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>61</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>103</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>62</td>\n",
       "      <td>59</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>105</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>62</td>\n",
       "      <td>56</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>63</td>\n",
       "      <td>46</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>109</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>63</td>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>63</td>\n",
       "      <td>48</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>111</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>63</td>\n",
       "      <td>52</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>71</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)  \\\n",
       "8             9       1   64                  19                       3   \n",
       "10           11       1   67                  19                      14   \n",
       "14           15       1   37                  20                      13   \n",
       "18           19       1   52                  23                      29   \n",
       "30           31       1   60                  30                       4   \n",
       "32           33       1   53                  33                       4   \n",
       "42           43       1   48                  39                      36   \n",
       "53           54       1   59                  43                      60   \n",
       "55           56       1   47                  43                      41   \n",
       "57           58       1   69                  44                      46   \n",
       "59           60       1   53                  46                      46   \n",
       "60           61       1   70                  46                      56   \n",
       "64           65       1   63                  48                      51   \n",
       "70           71       1   70                  49                      55   \n",
       "74           75       1   59                  54                      47   \n",
       "77           78       1   40                  54                      48   \n",
       "80           81       1   57                  54                      51   \n",
       "81           82       1   38                  54                      55   \n",
       "82           83       1   67                  54                      41   \n",
       "85           86       1   48                  54                      46   \n",
       "92           93       1   48                  60                      49   \n",
       "98           99       1   48                  61                      42   \n",
       "102         103       1   67                  62                      59   \n",
       "104         105       1   49                  62                      56   \n",
       "107         108       1   54                  63                      46   \n",
       "108         109       1   68                  63                      43   \n",
       "109         110       1   66                  63                      48   \n",
       "110         111       1   65                  63                      52   \n",
       "128         129       1   59                  71                      11   \n",
       "\n",
       "     Clus_km  \n",
       "8          4  \n",
       "10         4  \n",
       "14         4  \n",
       "18         4  \n",
       "30         4  \n",
       "32         4  \n",
       "42         4  \n",
       "53         4  \n",
       "55         4  \n",
       "57         4  \n",
       "59         4  \n",
       "60         4  \n",
       "64         4  \n",
       "70         4  \n",
       "74         4  \n",
       "77         4  \n",
       "80         4  \n",
       "81         4  \n",
       "82         4  \n",
       "85         4  \n",
       "92         4  \n",
       "98         4  \n",
       "102        4  \n",
       "104        4  \n",
       "107        4  \n",
       "108        4  \n",
       "109        4  \n",
       "110        4  \n",
       "128        4  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Clus_km\"] == 4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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   "source": [
    "# Initialize the plot with the specified dimensions.\n",
    "fig = plt.figure(figsize=(12, 10))\n",
    "\n",
    "# Colors uses a color map, which will produce an array of colors based on\n",
    "# the number of labels there are. We use set(k_means_labels) to get the\n",
    "# unique labels.\n",
    "colors = plt.cm.Spectral(np.linspace(0, 1, len(set(k_means_labels))))\n",
    "\n",
    "# Create a plot\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "# For loop that plots the data points and centroids.\n",
    "# k will range from 0-3, which will match the possible clusters that each\n",
    "# data point is in.\n",
    "for k, col in zip(range(5), colors):\n",
    "\n",
    "    # Create a list of all data points, where the data poitns that are \n",
    "    # in the cluster (ex. cluster 0) are labeled as true, else they are\n",
    "    # labeled as false.\n",
    "    my_members = (k_means_labels == k)\n",
    "    \n",
    "    # Define the centroid, or cluster center.\n",
    "    cluster_center = k_means_cluster_centers[k]\n",
    "    \n",
    "    # Plots the datapoints with color col.\n",
    "    ax.plot(Clus_dataSet[my_members, 0], Clus_dataSet[my_members, 1], 'w', markerfacecolor=col, marker='.')\n",
    "    \n",
    "    # Plots the centroids with specified color, but with a darker outline\n",
    "    ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,  markeredgecolor='k', markersize=6)\n",
    "\n",
    "# Title of the plot\n",
    "ax.set_title('KMeans')\n",
    "\n",
    "# Remove x-axis ticks\n",
    "ax.set_xticks(())\n",
    "\n",
    "# Remove y-axis ticks\n",
    "ax.set_yticks(())\n",
    "\n",
    "# Show the plot\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len([[4,4], [-2, -1], [2, -3], [1, 1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "[0.61960784 0.00392157 0.25882353 1.        ]\n",
      "1\n",
      "[0.95686275 0.42745098 0.2627451  1.        ]\n",
      "2\n",
      "[0.99607843 0.87843137 0.54509804 1.        ]\n",
      "3\n",
      "[0.90196078 0.96078431 0.59607843 1.        ]\n"
     ]
    }
   ],
   "source": [
    "for k, col in zip(range(len([[4,4], [-2, -1], [2, -3], [1, 1]])), colors):\n",
    "    print (k)\n",
    "    print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "[0.61960784 0.00392157 0.25882353 1.        ]\n",
      "1\n",
      "[0.95686275 0.42745098 0.2627451  1.        ]\n",
      "2\n",
      "[0.99607843 0.87843137 0.54509804 1.        ]\n",
      "3\n",
      "[0.90196078 0.96078431 0.59607843 1.        ]\n",
      "4\n",
      "[0.4        0.76078431 0.64705882 1.        ]\n",
      "5\n",
      "[0.36862745 0.30980392 0.63529412 1.        ]\n"
     ]
    }
   ],
   "source": [
    "for k, col in zip(range(6), colors):\n",
    "    print (k)\n",
    "    print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0, 1, 2, 3, 4, 5}"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(k_means_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.61960784, 0.00392157, 0.25882353, 1.        ],\n",
       "       [0.95686275, 0.42745098, 0.2627451 , 1.        ],\n",
       "       [0.99607843, 0.87843137, 0.54509804, 1.        ],\n",
       "       [0.90196078, 0.96078431, 0.59607843, 1.        ],\n",
       "       [0.4       , 0.76078431, 0.64705882, 1.        ],\n",
       "       [0.36862745, 0.30980392, 0.63529412, 1.        ]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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