mall_customers_clustering_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "52EKH075XlW9"
   },
   "source": [
    "**Mall Customers Clustering Analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_kg_hide-input": true,
    "id": "fYCzevADcO5h"
   },
   "outputs": [],
   "source": [
    "# for basic mathematics operation \n",
    "import numpy as np\n",
    "\n",
    "# for dataframe manipulations\n",
    "import pandas as pd\n",
    "\n",
    "# for Data Visualizations\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "# for data analysis\n",
    "import dabl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3CPkewAscpyK"
   },
   "source": [
    "**Reading the Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_kg_hide-input": true,
    "id": "dnV20cUxcohO"
   },
   "outputs": [],
   "source": [
    "# importing the dataset\n",
    "data = pd.read_csv('Mall_Customers.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e_xgv4lqXlXF"
   },
   "source": [
    "## Examining Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "Slr10zg-XlXF",
    "outputId": "6d422524-763e-4361-e31a-401580dcbdbe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the dataset : (200, 5)\n"
     ]
    }
   ],
   "source": [
    "# lets check the shape of the dataset\n",
    "print(\"Shape of the dataset :\", data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "xhO_MNxOXlXI",
    "outputId": "cd5f56db-d96f-4bea-d7a3-6b3722f17fa5"
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "\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$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Gender  Age  Annual Income (k$)  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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets check the head of the data\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "e_8yhC5DXlXK",
    "outputId": "6cbac127-164c-4731-ed8d-5a5094004372"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<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$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>196</td>\n",
       "      <td>Female</td>\n",
       "      <td>35</td>\n",
       "      <td>120</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>197</td>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "      <td>126</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>198</td>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>126</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>199</td>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>137</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>200</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>137</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\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"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets check the tail of the data\n",
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "4SaYgu4EXlXM",
    "outputId": "1977b6fe-eea8-4144-c3dc-ccbbf51009e5"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>39</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>154</td>\n",
       "      <td>Female</td>\n",
       "      <td>38</td>\n",
       "      <td>78</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
       "43           44  Female   31                  39                      61\n",
       "2             3  Female   20                  16                       6\n",
       "153         154  Female   38                  78                      76"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets check sample of the data\n",
    "data.sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h88DO2ZUXlXZ"
   },
   "source": [
    "## Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_kg_hide-input": true,
    "id": "kCddXZRpdaEr",
    "outputId": "a02f964b-bad9-4fca-de32-1cb8a535f407"
   },
   "outputs": [
    {
     "data": {
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       "</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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describing the data\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "JYtDrSPcXlXb",
    "outputId": "e6919c00-7157-4e28-e951-470c8e1301b7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Gender\n",
       "count      200\n",
       "unique       2\n",
       "top     Female\n",
       "freq       112"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describing the categorical data\n",
    "data.describe(include = 'object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "QQVxAt8ZdeZB",
    "outputId": "189bcfaa-3bc6-4ad1-8659-178a6444c161"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking if there is any NULL data\n",
    "data.isnull().any().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J5WYF5cQXlX1"
   },
   "source": [
    "## Clustering Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "worK1OurkRGx",
    "outputId": "f82ce7dd-9067-42ca-c8e0-20ca9ec32cdc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200, 2)\n"
     ]
    }
   ],
   "source": [
    "# we want to perform clusters of Customers who share similar behaviour for that lets select the columns\n",
    "# Spending score, and Annual Income\n",
    "\n",
    "# lets import the warnings library so that we can avoid warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Lets select the Spending score, and Annual Income Columns from the Data\n",
    "x = data.loc[:, ['Spending Score (1-100)', 'Annual Income (k$)']].values\n",
    "\n",
    "# let's check the shape of x\n",
    "print(x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "KdAIxNSfXlX3",
    "outputId": "bf405dde-ad16-47f8-ef2c-70d82fadf744"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>81</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>77</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "    0   1\n",
       "0  39  15\n",
       "1  81  15\n",
       "2   6  16\n",
       "3  77  16\n",
       "4  40  17"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets also check the data, which we are going to use for the clustering analysis\n",
    "x_data  = pd.DataFrame(x)\n",
    "x_data.head()\n",
    "# where o->Spending Score, and 1->Annual Income"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JRI9W3EAXlX4"
   },
   "source": [
    "## Kmeans Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L_jc_wEzXlX5"
   },
   "source": [
    "**The Elbow Method to find the No. of Optimal Clusters**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_kg_hide-input": true,
    "id": "vhf219y1lKbn",
    "outputId": "18f6cea1-7082-425d-a088-2c3eb2ab1b2a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "wcss = []\n",
    "for i in range(1, 11):\n",
    "    km = KMeans(n_clusters = i, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)\n",
    "    km.fit(x)\n",
    "    wcss.append(km.inertia_)\n",
    "    \n",
    "plt.plot(range(1, 11), wcss)\n",
    "plt.title('The Elbow Method', fontsize = 20)\n",
    "plt.xlabel('No. of Clusters')\n",
    "plt.ylabel('wcss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lBfbnlhXXlX7"
   },
   "source": [
    "**Visualizaing the Clusters**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_kg_hide-input": true,
    "id": "4teho5mpnARV",
    "outputId": "2859f139-a905-451c-b041-4c2adae48f19"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lets visualize these clusters\n",
    "\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "km = KMeans(n_clusters = 5, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)\n",
    "y_means = km.fit_predict(x)\n",
    "\n",
    "plt.scatter(x[y_means == 0, 0], x[y_means == 0, 1], s = 100, c = 'pink', label = 'miser')\n",
    "plt.scatter(x[y_means == 1, 0], x[y_means == 1, 1], s = 100, c = 'yellow', label = 'general')\n",
    "plt.scatter(x[y_means == 2, 0], x[y_means == 2, 1], s = 100, c = 'cyan', label = 'target')\n",
    "plt.scatter(x[y_means == 3, 0], x[y_means == 3, 1], s = 100, c = 'magenta', label = 'spendthrift')\n",
    "plt.scatter(x[y_means == 4, 0], x[y_means == 4, 1], s = 100, c = 'orange', label = 'careful')\n",
    "plt.scatter(km.cluster_centers_[:,0], km.cluster_centers_[:, 1], s = 50, c = 'blue' , label = 'centeroid')\n",
    "\n",
    "plt.style.use('fivethirtyeight')\n",
    "plt.title('K Means Clustering between Annual Income and Spending Score', fontsize = 20)\n",
    "plt.xlabel('Annual Income')\n",
    "plt.ylabel('Spending Score')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X2qnvfRmXlX9"
   },
   "source": [
    "### Clustering between Age and Spending Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_kg_hide-input": true,
    "id": "utqqEXGjmxNq",
    "outputId": "7bb54264-58e5-4ffb-de1f-052b981b7e1e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "wcss = []\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)\n",
    "    kmeans.fit(x)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (15, 5)\n",
    "plt.plot(range(1, 11), wcss)\n",
    "plt.title('K-Means Clustering(The Elbow Method)', fontsize = 20)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Count')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_kg_hide-input": true,
    "id": "jXc05lqRoGZ2",
    "outputId": "5b6edaaf-c6a7-4063-87cd-8b4d8175399c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters = 4, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)\n",
    "ymeans = kmeans.fit_predict(x)\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (10, 10)\n",
    "plt.title('Cluster of Ages', fontsize = 30)\n",
    "\n",
    "plt.scatter(x[ymeans == 0, 0], x[ymeans == 0, 1], s = 100, c = 'pink', label = 'Usual Customers' )\n",
    "plt.scatter(x[ymeans == 1, 0], x[ymeans == 1, 1], s = 100, c = 'orange', label = 'Priority Customers')\n",
    "plt.scatter(x[ymeans == 2, 0], x[ymeans == 2, 1], s = 100, c = 'lightgreen', label = 'Target Customers(Young)')\n",
    "plt.scatter(x[ymeans == 3, 0], x[ymeans == 3, 1], s = 100, c = 'red', label = 'Target Customers(Old)')\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 50, c = 'black')\n",
    "\n",
    "plt.style.use('fivethirtyeight')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Spending Score (1-100)')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "mall-customers-clustering-analysis.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}