08. K-Means Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\Mohan\\\\Desktop\\\\Others\\\\All Workshops\\\\NIT Jaipur\\\\Python Codes'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"C:/Users/Mohan/Desktop\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = pd.read_csv('Mall_Customers.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Female</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Female</td>\n",
       "      <td>35</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Male</td>\n",
       "      <td>64</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Female</td>\n",
       "      <td>30</td>\n",
       "      <td>19</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Male</td>\n",
       "      <td>67</td>\n",
       "      <td>19</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Female</td>\n",
       "      <td>35</td>\n",
       "      <td>19</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Female</td>\n",
       "      <td>58</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Male</td>\n",
       "      <td>37</td>\n",
       "      <td>20</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>Female</td>\n",
       "      <td>35</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>52</td>\n",
       "      <td>23</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>Female</td>\n",
       "      <td>35</td>\n",
       "      <td>23</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>24</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>Male</td>\n",
       "      <td>25</td>\n",
       "      <td>24</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>Female</td>\n",
       "      <td>46</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>Male</td>\n",
       "      <td>31</td>\n",
       "      <td>25</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>Female</td>\n",
       "      <td>54</td>\n",
       "      <td>28</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>Male</td>\n",
       "      <td>29</td>\n",
       "      <td>28</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "      <td>28</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>28</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>Female</td>\n",
       "      <td>40</td>\n",
       "      <td>29</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>29</td>\n",
       "      <td>87</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>170</th>\n",
       "      <td>171</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>87</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>172</td>\n",
       "      <td>Male</td>\n",
       "      <td>28</td>\n",
       "      <td>87</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>173</td>\n",
       "      <td>Male</td>\n",
       "      <td>36</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>174</td>\n",
       "      <td>Male</td>\n",
       "      <td>36</td>\n",
       "      <td>87</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>175</td>\n",
       "      <td>Female</td>\n",
       "      <td>52</td>\n",
       "      <td>88</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>176</td>\n",
       "      <td>Female</td>\n",
       "      <td>30</td>\n",
       "      <td>88</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>177</td>\n",
       "      <td>Male</td>\n",
       "      <td>58</td>\n",
       "      <td>88</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>178</td>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>88</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>179</td>\n",
       "      <td>Male</td>\n",
       "      <td>59</td>\n",
       "      <td>93</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>180</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>93</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>181</td>\n",
       "      <td>Female</td>\n",
       "      <td>37</td>\n",
       "      <td>97</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>182</td>\n",
       "      <td>Female</td>\n",
       "      <td>32</td>\n",
       "      <td>97</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>183</td>\n",
       "      <td>Male</td>\n",
       "      <td>46</td>\n",
       "      <td>98</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>184</td>\n",
       "      <td>Female</td>\n",
       "      <td>29</td>\n",
       "      <td>98</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>185</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>99</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>186</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>99</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>187</td>\n",
       "      <td>Female</td>\n",
       "      <td>54</td>\n",
       "      <td>101</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>188</td>\n",
       "      <td>Male</td>\n",
       "      <td>28</td>\n",
       "      <td>101</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>189</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>103</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>190</td>\n",
       "      <td>Female</td>\n",
       "      <td>36</td>\n",
       "      <td>103</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>191</td>\n",
       "      <td>Female</td>\n",
       "      <td>34</td>\n",
       "      <td>103</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>192</td>\n",
       "      <td>Female</td>\n",
       "      <td>32</td>\n",
       "      <td>103</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>193</td>\n",
       "      <td>Male</td>\n",
       "      <td>33</td>\n",
       "      <td>113</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>194</td>\n",
       "      <td>Female</td>\n",
       "      <td>38</td>\n",
       "      <td>113</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>195</td>\n",
       "      <td>Female</td>\n",
       "      <td>47</td>\n",
       "      <td>120</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>196</td>\n",
       "      <td>Female</td>\n",
       "      <td>35</td>\n",
       "      <td>120</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>197</td>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "      <td>126</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>198</td>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>126</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>199</td>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>137</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>200</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>137</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     CustomerID   Genre  Age  Annual Income (k$)  Spending Score (1-100)\n",
       "0             1    Male   19                  15                      39\n",
       "1             2    Male   21                  15                      81\n",
       "2             3  Female   20                  16                       6\n",
       "3             4  Female   23                  16                      77\n",
       "4             5  Female   31                  17                      40\n",
       "5             6  Female   22                  17                      76\n",
       "6             7  Female   35                  18                       6\n",
       "7             8  Female   23                  18                      94\n",
       "8             9    Male   64                  19                       3\n",
       "9            10  Female   30                  19                      72\n",
       "10           11    Male   67                  19                      14\n",
       "11           12  Female   35                  19                      99\n",
       "12           13  Female   58                  20                      15\n",
       "13           14  Female   24                  20                      77\n",
       "14           15    Male   37                  20                      13\n",
       "15           16    Male   22                  20                      79\n",
       "16           17  Female   35                  21                      35\n",
       "17           18    Male   20                  21                      66\n",
       "18           19    Male   52                  23                      29\n",
       "19           20  Female   35                  23                      98\n",
       "20           21    Male   35                  24                      35\n",
       "21           22    Male   25                  24                      73\n",
       "22           23  Female   46                  25                       5\n",
       "23           24    Male   31                  25                      73\n",
       "24           25  Female   54                  28                      14\n",
       "25           26    Male   29                  28                      82\n",
       "26           27  Female   45                  28                      32\n",
       "27           28    Male   35                  28                      61\n",
       "28           29  Female   40                  29                      31\n",
       "29           30  Female   23                  29                      87\n",
       "..          ...     ...  ...                 ...                     ...\n",
       "170         171    Male   40                  87                      13\n",
       "171         172    Male   28                  87                      75\n",
       "172         173    Male   36                  87                      10\n",
       "173         174    Male   36                  87                      92\n",
       "174         175  Female   52                  88                      13\n",
       "175         176  Female   30                  88                      86\n",
       "176         177    Male   58                  88                      15\n",
       "177         178    Male   27                  88                      69\n",
       "178         179    Male   59                  93                      14\n",
       "179         180    Male   35                  93                      90\n",
       "180         181  Female   37                  97                      32\n",
       "181         182  Female   32                  97                      86\n",
       "182         183    Male   46                  98                      15\n",
       "183         184  Female   29                  98                      88\n",
       "184         185  Female   41                  99                      39\n",
       "185         186    Male   30                  99                      97\n",
       "186         187  Female   54                 101                      24\n",
       "187         188    Male   28                 101                      68\n",
       "188         189  Female   41                 103                      17\n",
       "189         190  Female   36                 103                      85\n",
       "190         191  Female   34                 103                      23\n",
       "191         192  Female   32                 103                      69\n",
       "192         193    Male   33                 113                       8\n",
       "193         194  Female   38                 113                      91\n",
       "194         195  Female   47                 120                      16\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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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>CustomerID</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Female</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Female</td>\n",
       "      <td>35</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Male</td>\n",
       "      <td>64</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Female</td>\n",
       "      <td>30</td>\n",
       "      <td>19</td>\n",
       "      <td>72</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\n",
       "5           6  Female   22                  17                      76\n",
       "6           7  Female   35                  18                       6\n",
       "7           8  Female   23                  18                      94\n",
       "8           9    Male   64                  19                       3\n",
       "9          10  Female   30                  19                      72"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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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>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>190</th>\n",
       "      <td>191</td>\n",
       "      <td>Female</td>\n",
       "      <td>34</td>\n",
       "      <td>103</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>192</td>\n",
       "      <td>Female</td>\n",
       "      <td>32</td>\n",
       "      <td>103</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>193</td>\n",
       "      <td>Male</td>\n",
       "      <td>33</td>\n",
       "      <td>113</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>194</td>\n",
       "      <td>Female</td>\n",
       "      <td>38</td>\n",
       "      <td>113</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>195</td>\n",
       "      <td>Female</td>\n",
       "      <td>47</td>\n",
       "      <td>120</td>\n",
       "      <td>16</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",
       "</div>"
      ],
      "text/plain": [
       "     CustomerID   Genre  Age  Annual Income (k$)  Spending Score (1-100)\n",
       "190         191  Female   34                 103                      23\n",
       "191         192  Female   32                 103                      69\n",
       "192         193    Male   33                 113                       8\n",
       "193         194  Female   38                 113                      91\n",
       "194         195  Female   47                 120                      16\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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 5)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get the shape of the dataframe\n",
    "data1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 5 columns):\n",
      "CustomerID                200 non-null int64\n",
      "Genre                     200 non-null object\n",
      "Age                       200 non-null int64\n",
      "Annual Income (k$)        200 non-null int64\n",
      "Spending Score (1-100)    200 non-null int64\n",
      "dtypes: int64(4), object(1)\n",
      "memory usage: 7.9+ KB\n"
     ]
    }
   ],
   "source": [
    "data1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID                0\n",
       "Genre                     0\n",
       "Age                       0\n",
       "Annual Income (k$)        0\n",
       "Spending Score (1-100)    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Compute Missing values\n",
    "data1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.026763</td>\n",
       "      <td>0.977548</td>\n",
       "      <td>0.013835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>-0.026763</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012398</td>\n",
       "      <td>-0.327227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Annual Income (k$)</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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Feature Selection for Model\n",
    "#Consider 2 features (Annual Income and Spending Score)\n",
    "X= data1.iloc[:, [3,4]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "       [ 99,  97],\n",
       "       [101,  24],\n",
       "       [101,  68],\n",
       "       [103,  17],\n",
       "       [103,  85],\n",
       "       [103,  23],\n",
       "       [103,  69],\n",
       "       [113,   8],\n",
       "       [113,  91],\n",
       "       [120,  16],\n",
       "       [120,  79],\n",
       "       [126,  28],\n",
       "       [126,  74],\n",
       "       [137,  18],\n",
       "       [137,  83]], dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Build the K Means cluster model\n",
    "from sklearn.cluster import KMeans\n",
    "wcss=[]\n",
    "#Static code to get max no of clusters using inertia to seggregate the data\n",
    "#points into clusters\n",
    "\n",
    "for i in range(1,11):\n",
    "    kmeans = KMeans(n_clusters= i, init='k-means++', random_state=0)\n",
    "    kmeans.fit(X)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c8a36ef320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualizing the ELBOW method to get the optimal value of K \n",
    "plt.plot(range(1,11), wcss)\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('no of clusters')\n",
    "plt.ylabel('wcss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Zoom out the curve to see the last elbow\n",
    "#No matter what range we select ex- (1,21) also i will see the same behaviour but if we chose higher range it is little difficult to visualize the ELBOW\n",
    "#that is why usually prefer range (1,11)\n",
    "##Finally we got that k=5\n",
    "#Build the model\n",
    "kmeansmodel = KMeans(n_clusters= 5, init='k-means++', random_state=0)\n",
    "y_kmeans= kmeansmodel.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#For unsupervised learning we use \"fit_predict()\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c8a620cf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#For unsupervised learning we use \"fit_predict()\"\n",
    "#y_kmeans is the final model . Now how and where we will deploy this model in \n",
    "#production is depends on what tool we are using.\n",
    "#Visualizing all the clusters \n",
    "\n",
    "plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Cluster 1')\n",
    "plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')\n",
    "plt.scatter(X[y_kmeans == 2, 0], X[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Cluster 3')\n",
    "plt.scatter(X[y_kmeans == 3, 0], X[y_kmeans == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')\n",
    "plt.scatter(X[y_kmeans == 4, 0], X[y_kmeans == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroids')\n",
    "plt.title('Clusters of customers')\n",
    "plt.xlabel('Annual Income (k$)')\n",
    "plt.ylabel('Spending Score (1-100)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Interpretation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cluster 1 (Red Color) -> earning high but spending less\n",
    "#cluster 2 (Blue Col0r) -> average in terms of earning and spending \n",
    "#cluster 3 (Green Color) -> earning high and also spending high [TARGET SET]\n",
    "#cluster 4 (cyan Color) -> earning less but spending more\n",
    "#Cluster 5 (magenta Color) -> Earning less , spending less"
   ]
  }
 ],
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