Assignment-07-DBSCAN Clustering (Crimes).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment-07-DBSCAN Clustering (Crimes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>13.2</td>\n",
       "      <td>236</td>\n",
       "      <td>58</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>10.0</td>\n",
       "      <td>263</td>\n",
       "      <td>48</td>\n",
       "      <td>44.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>8.1</td>\n",
       "      <td>294</td>\n",
       "      <td>80</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>8.8</td>\n",
       "      <td>190</td>\n",
       "      <td>50</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>9.0</td>\n",
       "      <td>276</td>\n",
       "      <td>91</td>\n",
       "      <td>40.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>7.9</td>\n",
       "      <td>204</td>\n",
       "      <td>78</td>\n",
       "      <td>38.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>3.3</td>\n",
       "      <td>110</td>\n",
       "      <td>77</td>\n",
       "      <td>11.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>5.9</td>\n",
       "      <td>238</td>\n",
       "      <td>72</td>\n",
       "      <td>15.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Florida</td>\n",
       "      <td>15.4</td>\n",
       "      <td>335</td>\n",
       "      <td>80</td>\n",
       "      <td>31.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>17.4</td>\n",
       "      <td>211</td>\n",
       "      <td>60</td>\n",
       "      <td>25.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>5.3</td>\n",
       "      <td>46</td>\n",
       "      <td>83</td>\n",
       "      <td>20.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>2.6</td>\n",
       "      <td>120</td>\n",
       "      <td>54</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>10.4</td>\n",
       "      <td>249</td>\n",
       "      <td>83</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>7.2</td>\n",
       "      <td>113</td>\n",
       "      <td>65</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Iowa</td>\n",
       "      <td>2.2</td>\n",
       "      <td>56</td>\n",
       "      <td>57</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Kansas</td>\n",
       "      <td>6.0</td>\n",
       "      <td>115</td>\n",
       "      <td>66</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>9.7</td>\n",
       "      <td>109</td>\n",
       "      <td>52</td>\n",
       "      <td>16.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Louisiana</td>\n",
       "      <td>15.4</td>\n",
       "      <td>249</td>\n",
       "      <td>66</td>\n",
       "      <td>22.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Maine</td>\n",
       "      <td>2.1</td>\n",
       "      <td>83</td>\n",
       "      <td>51</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Maryland</td>\n",
       "      <td>11.3</td>\n",
       "      <td>300</td>\n",
       "      <td>67</td>\n",
       "      <td>27.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>4.4</td>\n",
       "      <td>149</td>\n",
       "      <td>85</td>\n",
       "      <td>16.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>12.1</td>\n",
       "      <td>255</td>\n",
       "      <td>74</td>\n",
       "      <td>35.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>2.7</td>\n",
       "      <td>72</td>\n",
       "      <td>66</td>\n",
       "      <td>14.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Mississippi</td>\n",
       "      <td>16.1</td>\n",
       "      <td>259</td>\n",
       "      <td>44</td>\n",
       "      <td>17.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Missouri</td>\n",
       "      <td>9.0</td>\n",
       "      <td>178</td>\n",
       "      <td>70</td>\n",
       "      <td>28.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Montana</td>\n",
       "      <td>6.0</td>\n",
       "      <td>109</td>\n",
       "      <td>53</td>\n",
       "      <td>16.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Nebraska</td>\n",
       "      <td>4.3</td>\n",
       "      <td>102</td>\n",
       "      <td>62</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>12.2</td>\n",
       "      <td>252</td>\n",
       "      <td>81</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>2.1</td>\n",
       "      <td>57</td>\n",
       "      <td>56</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>New Jersey</td>\n",
       "      <td>7.4</td>\n",
       "      <td>159</td>\n",
       "      <td>89</td>\n",
       "      <td>18.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>11.4</td>\n",
       "      <td>285</td>\n",
       "      <td>70</td>\n",
       "      <td>32.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>New York</td>\n",
       "      <td>11.1</td>\n",
       "      <td>254</td>\n",
       "      <td>86</td>\n",
       "      <td>26.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>13.0</td>\n",
       "      <td>337</td>\n",
       "      <td>45</td>\n",
       "      <td>16.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>0.8</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>7.3</td>\n",
       "      <td>120</td>\n",
       "      <td>75</td>\n",
       "      <td>21.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>6.6</td>\n",
       "      <td>151</td>\n",
       "      <td>68</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>4.9</td>\n",
       "      <td>159</td>\n",
       "      <td>67</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>6.3</td>\n",
       "      <td>106</td>\n",
       "      <td>72</td>\n",
       "      <td>14.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>3.4</td>\n",
       "      <td>174</td>\n",
       "      <td>87</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>South Carolina</td>\n",
       "      <td>14.4</td>\n",
       "      <td>279</td>\n",
       "      <td>48</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>South Dakota</td>\n",
       "      <td>3.8</td>\n",
       "      <td>86</td>\n",
       "      <td>45</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>13.2</td>\n",
       "      <td>188</td>\n",
       "      <td>59</td>\n",
       "      <td>26.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Texas</td>\n",
       "      <td>12.7</td>\n",
       "      <td>201</td>\n",
       "      <td>80</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Utah</td>\n",
       "      <td>3.2</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>22.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>2.2</td>\n",
       "      <td>48</td>\n",
       "      <td>32</td>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>8.5</td>\n",
       "      <td>156</td>\n",
       "      <td>63</td>\n",
       "      <td>20.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Washington</td>\n",
       "      <td>4.0</td>\n",
       "      <td>145</td>\n",
       "      <td>73</td>\n",
       "      <td>26.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>5.7</td>\n",
       "      <td>81</td>\n",
       "      <td>39</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>2.6</td>\n",
       "      <td>53</td>\n",
       "      <td>66</td>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>6.8</td>\n",
       "      <td>161</td>\n",
       "      <td>60</td>\n",
       "      <td>15.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  Murder  Assault  UrbanPop  Rape\n",
       "0          Alabama    13.2      236        58  21.2\n",
       "1           Alaska    10.0      263        48  44.5\n",
       "2          Arizona     8.1      294        80  31.0\n",
       "3         Arkansas     8.8      190        50  19.5\n",
       "4       California     9.0      276        91  40.6\n",
       "5         Colorado     7.9      204        78  38.7\n",
       "6      Connecticut     3.3      110        77  11.1\n",
       "7         Delaware     5.9      238        72  15.8\n",
       "8          Florida    15.4      335        80  31.9\n",
       "9          Georgia    17.4      211        60  25.8\n",
       "10          Hawaii     5.3       46        83  20.2\n",
       "11           Idaho     2.6      120        54  14.2\n",
       "12        Illinois    10.4      249        83  24.0\n",
       "13         Indiana     7.2      113        65  21.0\n",
       "14            Iowa     2.2       56        57  11.3\n",
       "15          Kansas     6.0      115        66  18.0\n",
       "16        Kentucky     9.7      109        52  16.3\n",
       "17       Louisiana    15.4      249        66  22.2\n",
       "18           Maine     2.1       83        51   7.8\n",
       "19        Maryland    11.3      300        67  27.8\n",
       "20   Massachusetts     4.4      149        85  16.3\n",
       "21        Michigan    12.1      255        74  35.1\n",
       "22       Minnesota     2.7       72        66  14.9\n",
       "23     Mississippi    16.1      259        44  17.1\n",
       "24        Missouri     9.0      178        70  28.2\n",
       "25         Montana     6.0      109        53  16.4\n",
       "26        Nebraska     4.3      102        62  16.5\n",
       "27          Nevada    12.2      252        81  46.0\n",
       "28   New Hampshire     2.1       57        56   9.5\n",
       "29      New Jersey     7.4      159        89  18.8\n",
       "30      New Mexico    11.4      285        70  32.1\n",
       "31        New York    11.1      254        86  26.1\n",
       "32  North Carolina    13.0      337        45  16.1\n",
       "33    North Dakota     0.8       45        44   7.3\n",
       "34            Ohio     7.3      120        75  21.4\n",
       "35        Oklahoma     6.6      151        68  20.0\n",
       "36          Oregon     4.9      159        67  29.3\n",
       "37    Pennsylvania     6.3      106        72  14.9\n",
       "38    Rhode Island     3.4      174        87   8.3\n",
       "39  South Carolina    14.4      279        48  22.5\n",
       "40    South Dakota     3.8       86        45  12.8\n",
       "41       Tennessee    13.2      188        59  26.9\n",
       "42           Texas    12.7      201        80  25.5\n",
       "43            Utah     3.2      120        80  22.9\n",
       "44         Vermont     2.2       48        32  11.2\n",
       "45        Virginia     8.5      156        63  20.7\n",
       "46      Washington     4.0      145        73  26.2\n",
       "47   West Virginia     5.7       81        39   9.3\n",
       "48       Wisconsin     2.6       53        66  10.8\n",
       "49         Wyoming     6.8      161        60  15.6"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import Dataset\n",
    "crime=pd.read_csv('Database/crime_data.csv')\n",
    "crime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50 entries, 0 to 49\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Unnamed: 0  50 non-null     object \n",
      " 1   Murder      50 non-null     float64\n",
      " 2   Assault     50 non-null     int64  \n",
      " 3   UrbanPop    50 non-null     int64  \n",
      " 4   Rape        50 non-null     float64\n",
      "dtypes: float64(2), int64(2), object(1)\n",
      "memory usage: 2.1+ KB\n"
     ]
    }
   ],
   "source": [
    "crime.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.2</td>\n",
       "      <td>236</td>\n",
       "      <td>58</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.0</td>\n",
       "      <td>263</td>\n",
       "      <td>48</td>\n",
       "      <td>44.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>294</td>\n",
       "      <td>80</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.8</td>\n",
       "      <td>190</td>\n",
       "      <td>50</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.0</td>\n",
       "      <td>276</td>\n",
       "      <td>91</td>\n",
       "      <td>40.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.9</td>\n",
       "      <td>204</td>\n",
       "      <td>78</td>\n",
       "      <td>38.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.3</td>\n",
       "      <td>110</td>\n",
       "      <td>77</td>\n",
       "      <td>11.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.9</td>\n",
       "      <td>238</td>\n",
       "      <td>72</td>\n",
       "      <td>15.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.4</td>\n",
       "      <td>335</td>\n",
       "      <td>80</td>\n",
       "      <td>31.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>17.4</td>\n",
       "      <td>211</td>\n",
       "      <td>60</td>\n",
       "      <td>25.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.3</td>\n",
       "      <td>46</td>\n",
       "      <td>83</td>\n",
       "      <td>20.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.6</td>\n",
       "      <td>120</td>\n",
       "      <td>54</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10.4</td>\n",
       "      <td>249</td>\n",
       "      <td>83</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7.2</td>\n",
       "      <td>113</td>\n",
       "      <td>65</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.2</td>\n",
       "      <td>56</td>\n",
       "      <td>57</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6.0</td>\n",
       "      <td>115</td>\n",
       "      <td>66</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9.7</td>\n",
       "      <td>109</td>\n",
       "      <td>52</td>\n",
       "      <td>16.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>15.4</td>\n",
       "      <td>249</td>\n",
       "      <td>66</td>\n",
       "      <td>22.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.1</td>\n",
       "      <td>83</td>\n",
       "      <td>51</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11.3</td>\n",
       "      <td>300</td>\n",
       "      <td>67</td>\n",
       "      <td>27.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4.4</td>\n",
       "      <td>149</td>\n",
       "      <td>85</td>\n",
       "      <td>16.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>12.1</td>\n",
       "      <td>255</td>\n",
       "      <td>74</td>\n",
       "      <td>35.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2.7</td>\n",
       "      <td>72</td>\n",
       "      <td>66</td>\n",
       "      <td>14.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>16.1</td>\n",
       "      <td>259</td>\n",
       "      <td>44</td>\n",
       "      <td>17.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>9.0</td>\n",
       "      <td>178</td>\n",
       "      <td>70</td>\n",
       "      <td>28.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>6.0</td>\n",
       "      <td>109</td>\n",
       "      <td>53</td>\n",
       "      <td>16.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4.3</td>\n",
       "      <td>102</td>\n",
       "      <td>62</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>12.2</td>\n",
       "      <td>252</td>\n",
       "      <td>81</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2.1</td>\n",
       "      <td>57</td>\n",
       "      <td>56</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>7.4</td>\n",
       "      <td>159</td>\n",
       "      <td>89</td>\n",
       "      <td>18.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>11.4</td>\n",
       "      <td>285</td>\n",
       "      <td>70</td>\n",
       "      <td>32.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>11.1</td>\n",
       "      <td>254</td>\n",
       "      <td>86</td>\n",
       "      <td>26.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>13.0</td>\n",
       "      <td>337</td>\n",
       "      <td>45</td>\n",
       "      <td>16.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.8</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>7.3</td>\n",
       "      <td>120</td>\n",
       "      <td>75</td>\n",
       "      <td>21.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>6.6</td>\n",
       "      <td>151</td>\n",
       "      <td>68</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>4.9</td>\n",
       "      <td>159</td>\n",
       "      <td>67</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>6.3</td>\n",
       "      <td>106</td>\n",
       "      <td>72</td>\n",
       "      <td>14.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>3.4</td>\n",
       "      <td>174</td>\n",
       "      <td>87</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>14.4</td>\n",
       "      <td>279</td>\n",
       "      <td>48</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>3.8</td>\n",
       "      <td>86</td>\n",
       "      <td>45</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>13.2</td>\n",
       "      <td>188</td>\n",
       "      <td>59</td>\n",
       "      <td>26.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>12.7</td>\n",
       "      <td>201</td>\n",
       "      <td>80</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>3.2</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>22.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2.2</td>\n",
       "      <td>48</td>\n",
       "      <td>32</td>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>8.5</td>\n",
       "      <td>156</td>\n",
       "      <td>63</td>\n",
       "      <td>20.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>4.0</td>\n",
       "      <td>145</td>\n",
       "      <td>73</td>\n",
       "      <td>26.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>5.7</td>\n",
       "      <td>81</td>\n",
       "      <td>39</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2.6</td>\n",
       "      <td>53</td>\n",
       "      <td>66</td>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>6.8</td>\n",
       "      <td>161</td>\n",
       "      <td>60</td>\n",
       "      <td>15.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Murder  Assault  UrbanPop  Rape\n",
       "0     13.2      236        58  21.2\n",
       "1     10.0      263        48  44.5\n",
       "2      8.1      294        80  31.0\n",
       "3      8.8      190        50  19.5\n",
       "4      9.0      276        91  40.6\n",
       "5      7.9      204        78  38.7\n",
       "6      3.3      110        77  11.1\n",
       "7      5.9      238        72  15.8\n",
       "8     15.4      335        80  31.9\n",
       "9     17.4      211        60  25.8\n",
       "10     5.3       46        83  20.2\n",
       "11     2.6      120        54  14.2\n",
       "12    10.4      249        83  24.0\n",
       "13     7.2      113        65  21.0\n",
       "14     2.2       56        57  11.3\n",
       "15     6.0      115        66  18.0\n",
       "16     9.7      109        52  16.3\n",
       "17    15.4      249        66  22.2\n",
       "18     2.1       83        51   7.8\n",
       "19    11.3      300        67  27.8\n",
       "20     4.4      149        85  16.3\n",
       "21    12.1      255        74  35.1\n",
       "22     2.7       72        66  14.9\n",
       "23    16.1      259        44  17.1\n",
       "24     9.0      178        70  28.2\n",
       "25     6.0      109        53  16.4\n",
       "26     4.3      102        62  16.5\n",
       "27    12.2      252        81  46.0\n",
       "28     2.1       57        56   9.5\n",
       "29     7.4      159        89  18.8\n",
       "30    11.4      285        70  32.1\n",
       "31    11.1      254        86  26.1\n",
       "32    13.0      337        45  16.1\n",
       "33     0.8       45        44   7.3\n",
       "34     7.3      120        75  21.4\n",
       "35     6.6      151        68  20.0\n",
       "36     4.9      159        67  29.3\n",
       "37     6.3      106        72  14.9\n",
       "38     3.4      174        87   8.3\n",
       "39    14.4      279        48  22.5\n",
       "40     3.8       86        45  12.8\n",
       "41    13.2      188        59  26.9\n",
       "42    12.7      201        80  25.5\n",
       "43     3.2      120        80  22.9\n",
       "44     2.2       48        32  11.2\n",
       "45     8.5      156        63  20.7\n",
       "46     4.0      145        73  26.2\n",
       "47     5.7       81        39   9.3\n",
       "48     2.6       53        66  10.8\n",
       "49     6.8      161        60  15.6"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime.drop(['Unnamed: 0'],axis=1,inplace=True)\n",
    "crime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.25517927,  0.79078716, -0.52619514, -0.00345116],\n",
       "       [ 0.51301858,  1.11805959, -1.22406668,  2.50942392],\n",
       "       [ 0.07236067,  1.49381682,  1.00912225,  1.05346626],\n",
       "       [ 0.23470832,  0.23321191, -1.08449238, -0.18679398],\n",
       "       [ 0.28109336,  1.2756352 ,  1.77678094,  2.08881393],\n",
       "       [ 0.02597562,  0.40290872,  0.86954794,  1.88390137],\n",
       "       [-1.04088037, -0.73648418,  0.79976079, -1.09272319],\n",
       "       [-0.43787481,  0.81502956,  0.45082502, -0.58583422],\n",
       "       [ 1.76541475,  1.99078607,  1.00912225,  1.1505301 ],\n",
       "       [ 2.22926518,  0.48775713, -0.38662083,  0.49265293],\n",
       "       [-0.57702994, -1.51224105,  1.21848371, -0.11129987],\n",
       "       [-1.20322802, -0.61527217, -0.80534376, -0.75839217],\n",
       "       [ 0.60578867,  0.94836277,  1.21848371,  0.29852525],\n",
       "       [-0.13637203, -0.70012057, -0.03768506, -0.0250209 ],\n",
       "       [-1.29599811, -1.39102904, -0.5959823 , -1.07115345],\n",
       "       [-0.41468229, -0.67587817,  0.03210209, -0.34856705],\n",
       "       [ 0.44344101, -0.74860538, -0.94491807, -0.53190987],\n",
       "       [ 1.76541475,  0.94836277,  0.03210209,  0.10439756],\n",
       "       [-1.31919063, -1.06375661, -1.01470522, -1.44862395],\n",
       "       [ 0.81452136,  1.56654403,  0.10188925,  0.70835037],\n",
       "       [-0.78576263, -0.26375734,  1.35805802, -0.53190987],\n",
       "       [ 1.00006153,  1.02108998,  0.59039932,  1.49564599],\n",
       "       [-1.1800355 , -1.19708982,  0.03210209, -0.68289807],\n",
       "       [ 1.9277624 ,  1.06957478, -1.5032153 , -0.44563089],\n",
       "       [ 0.28109336,  0.0877575 ,  0.31125071,  0.75148985],\n",
       "       [-0.41468229, -0.74860538, -0.87513091, -0.521125  ],\n",
       "       [-0.80895515, -0.83345379, -0.24704653, -0.51034012],\n",
       "       [ 1.02325405,  0.98472638,  1.0789094 ,  2.671197  ],\n",
       "       [-1.31919063, -1.37890783, -0.66576945, -1.26528114],\n",
       "       [-0.08998698, -0.14254532,  1.63720664, -0.26228808],\n",
       "       [ 0.83771388,  1.38472601,  0.31125071,  1.17209984],\n",
       "       [ 0.76813632,  1.00896878,  1.42784517,  0.52500755],\n",
       "       [ 1.20879423,  2.01502847, -1.43342815, -0.55347961],\n",
       "       [-1.62069341, -1.52436225, -1.5032153 , -1.50254831],\n",
       "       [-0.11317951, -0.61527217,  0.66018648,  0.01811858],\n",
       "       [-0.27552716, -0.23951493,  0.1716764 , -0.13286962],\n",
       "       [-0.66980002, -0.14254532,  0.10188925,  0.87012344],\n",
       "       [-0.34510472, -0.78496898,  0.45082502, -0.68289807],\n",
       "       [-1.01768785,  0.03927269,  1.49763233, -1.39469959],\n",
       "       [ 1.53348953,  1.3119988 , -1.22406668,  0.13675217],\n",
       "       [-0.92491776, -1.027393  , -1.43342815, -0.90938037],\n",
       "       [ 1.25517927,  0.20896951, -0.45640799,  0.61128652],\n",
       "       [ 1.13921666,  0.36654512,  1.00912225,  0.46029832],\n",
       "       [-1.06407289, -0.61527217,  1.00912225,  0.17989166],\n",
       "       [-1.29599811, -1.48799864, -2.34066115, -1.08193832],\n",
       "       [ 0.16513075, -0.17890893, -0.17725937, -0.05737552],\n",
       "       [-0.87853272, -0.31224214,  0.52061217,  0.53579242],\n",
       "       [-0.48425985, -1.08799901, -1.85215107, -1.28685088],\n",
       "       [-1.20322802, -1.42739264,  0.03210209, -1.1250778 ],\n",
       "       [-0.22914211, -0.11830292, -0.38662083, -0.60740397]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalize heterogenous numerical data using standard scalar fit transform to dataset\n",
    "crime_norm=StandardScaler().fit_transform(crime)\n",
    "crime_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DBSCAN(eps=1, min_samples=4)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DBSCAN Clustering\n",
    "dbscan=DBSCAN(eps=1,min_samples=4)\n",
    "dbscan.fit(crime_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0, -1, -1, -1, -1, -1,  1, -1, -1, -1, -1,  1, -1,  1,  1,  1,  1,\n",
       "        0,  1, -1,  1, -1,  1, -1,  1,  1,  1, -1,  1,  1, -1, -1, -1,  1,\n",
       "        1,  1,  1,  1,  1,  0,  1,  0, -1,  1,  1,  1,  1,  1,  1,  1],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Noisy samples are given the label -1.\n",
    "dbscan.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "      <th>clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.2</td>\n",
       "      <td>236</td>\n",
       "      <td>58</td>\n",
       "      <td>21.2</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>10.0</td>\n",
       "      <td>263</td>\n",
       "      <td>48</td>\n",
       "      <td>44.5</td>\n",
       "      <td>-1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>294</td>\n",
       "      <td>80</td>\n",
       "      <td>31.0</td>\n",
       "      <td>-1</td>\n",
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       "      <th>3</th>\n",
       "      <td>8.8</td>\n",
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       "      <td>50</td>\n",
       "      <td>19.5</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.0</td>\n",
       "      <td>276</td>\n",
       "      <td>91</td>\n",
       "      <td>40.6</td>\n",
       "      <td>-1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.9</td>\n",
       "      <td>204</td>\n",
       "      <td>78</td>\n",
       "      <td>38.7</td>\n",
       "      <td>-1</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.3</td>\n",
       "      <td>110</td>\n",
       "      <td>77</td>\n",
       "      <td>11.1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.9</td>\n",
       "      <td>238</td>\n",
       "      <td>72</td>\n",
       "      <td>15.8</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.4</td>\n",
       "      <td>335</td>\n",
       "      <td>80</td>\n",
       "      <td>31.9</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>17.4</td>\n",
       "      <td>211</td>\n",
       "      <td>60</td>\n",
       "      <td>25.8</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.3</td>\n",
       "      <td>46</td>\n",
       "      <td>83</td>\n",
       "      <td>20.2</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.6</td>\n",
       "      <td>120</td>\n",
       "      <td>54</td>\n",
       "      <td>14.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10.4</td>\n",
       "      <td>249</td>\n",
       "      <td>83</td>\n",
       "      <td>24.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7.2</td>\n",
       "      <td>113</td>\n",
       "      <td>65</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.2</td>\n",
       "      <td>56</td>\n",
       "      <td>57</td>\n",
       "      <td>11.3</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6.0</td>\n",
       "      <td>115</td>\n",
       "      <td>66</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9.7</td>\n",
       "      <td>109</td>\n",
       "      <td>52</td>\n",
       "      <td>16.3</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>15.4</td>\n",
       "      <td>249</td>\n",
       "      <td>66</td>\n",
       "      <td>22.2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.1</td>\n",
       "      <td>83</td>\n",
       "      <td>51</td>\n",
       "      <td>7.8</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11.3</td>\n",
       "      <td>300</td>\n",
       "      <td>67</td>\n",
       "      <td>27.8</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4.4</td>\n",
       "      <td>149</td>\n",
       "      <td>85</td>\n",
       "      <td>16.3</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>12.1</td>\n",
       "      <td>255</td>\n",
       "      <td>74</td>\n",
       "      <td>35.1</td>\n",
       "      <td>-1</td>\n",
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       "      <th>22</th>\n",
       "      <td>2.7</td>\n",
       "      <td>72</td>\n",
       "      <td>66</td>\n",
       "      <td>14.9</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>16.1</td>\n",
       "      <td>259</td>\n",
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       "      <td>17.1</td>\n",
       "      <td>-1</td>\n",
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       "      <th>24</th>\n",
       "      <td>9.0</td>\n",
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       "      <td>1</td>\n",
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       "      <th>25</th>\n",
       "      <td>6.0</td>\n",
       "      <td>109</td>\n",
       "      <td>53</td>\n",
       "      <td>16.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4.3</td>\n",
       "      <td>102</td>\n",
       "      <td>62</td>\n",
       "      <td>16.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>12.2</td>\n",
       "      <td>252</td>\n",
       "      <td>81</td>\n",
       "      <td>46.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2.1</td>\n",
       "      <td>57</td>\n",
       "      <td>56</td>\n",
       "      <td>9.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>7.4</td>\n",
       "      <td>159</td>\n",
       "      <td>89</td>\n",
       "      <td>18.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>11.4</td>\n",
       "      <td>285</td>\n",
       "      <td>70</td>\n",
       "      <td>32.1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>11.1</td>\n",
       "      <td>254</td>\n",
       "      <td>86</td>\n",
       "      <td>26.1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>13.0</td>\n",
       "      <td>337</td>\n",
       "      <td>45</td>\n",
       "      <td>16.1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.8</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "      <td>7.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>7.3</td>\n",
       "      <td>120</td>\n",
       "      <td>75</td>\n",
       "      <td>21.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>6.6</td>\n",
       "      <td>151</td>\n",
       "      <td>68</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>4.9</td>\n",
       "      <td>159</td>\n",
       "      <td>67</td>\n",
       "      <td>29.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>6.3</td>\n",
       "      <td>106</td>\n",
       "      <td>72</td>\n",
       "      <td>14.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>3.4</td>\n",
       "      <td>174</td>\n",
       "      <td>87</td>\n",
       "      <td>8.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>14.4</td>\n",
       "      <td>279</td>\n",
       "      <td>48</td>\n",
       "      <td>22.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>3.8</td>\n",
       "      <td>86</td>\n",
       "      <td>45</td>\n",
       "      <td>12.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>13.2</td>\n",
       "      <td>188</td>\n",
       "      <td>59</td>\n",
       "      <td>26.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>12.7</td>\n",
       "      <td>201</td>\n",
       "      <td>80</td>\n",
       "      <td>25.5</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>3.2</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>22.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2.2</td>\n",
       "      <td>48</td>\n",
       "      <td>32</td>\n",
       "      <td>11.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>8.5</td>\n",
       "      <td>156</td>\n",
       "      <td>63</td>\n",
       "      <td>20.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>4.0</td>\n",
       "      <td>145</td>\n",
       "      <td>73</td>\n",
       "      <td>26.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>5.7</td>\n",
       "      <td>81</td>\n",
       "      <td>39</td>\n",
       "      <td>9.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2.6</td>\n",
       "      <td>53</td>\n",
       "      <td>66</td>\n",
       "      <td>10.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>6.8</td>\n",
       "      <td>161</td>\n",
       "      <td>60</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Murder  Assault  UrbanPop  Rape  clusters\n",
       "0     13.2      236        58  21.2         0\n",
       "1     10.0      263        48  44.5        -1\n",
       "2      8.1      294        80  31.0        -1\n",
       "3      8.8      190        50  19.5        -1\n",
       "4      9.0      276        91  40.6        -1\n",
       "5      7.9      204        78  38.7        -1\n",
       "6      3.3      110        77  11.1         1\n",
       "7      5.9      238        72  15.8        -1\n",
       "8     15.4      335        80  31.9        -1\n",
       "9     17.4      211        60  25.8        -1\n",
       "10     5.3       46        83  20.2        -1\n",
       "11     2.6      120        54  14.2         1\n",
       "12    10.4      249        83  24.0        -1\n",
       "13     7.2      113        65  21.0         1\n",
       "14     2.2       56        57  11.3         1\n",
       "15     6.0      115        66  18.0         1\n",
       "16     9.7      109        52  16.3         1\n",
       "17    15.4      249        66  22.2         0\n",
       "18     2.1       83        51   7.8         1\n",
       "19    11.3      300        67  27.8        -1\n",
       "20     4.4      149        85  16.3         1\n",
       "21    12.1      255        74  35.1        -1\n",
       "22     2.7       72        66  14.9         1\n",
       "23    16.1      259        44  17.1        -1\n",
       "24     9.0      178        70  28.2         1\n",
       "25     6.0      109        53  16.4         1\n",
       "26     4.3      102        62  16.5         1\n",
       "27    12.2      252        81  46.0        -1\n",
       "28     2.1       57        56   9.5         1\n",
       "29     7.4      159        89  18.8         1\n",
       "30    11.4      285        70  32.1        -1\n",
       "31    11.1      254        86  26.1        -1\n",
       "32    13.0      337        45  16.1        -1\n",
       "33     0.8       45        44   7.3         1\n",
       "34     7.3      120        75  21.4         1\n",
       "35     6.6      151        68  20.0         1\n",
       "36     4.9      159        67  29.3         1\n",
       "37     6.3      106        72  14.9         1\n",
       "38     3.4      174        87   8.3         1\n",
       "39    14.4      279        48  22.5         0\n",
       "40     3.8       86        45  12.8         1\n",
       "41    13.2      188        59  26.9         0\n",
       "42    12.7      201        80  25.5        -1\n",
       "43     3.2      120        80  22.9         1\n",
       "44     2.2       48        32  11.2         1\n",
       "45     8.5      156        63  20.7         1\n",
       "46     4.0      145        73  26.2         1\n",
       "47     5.7       81        39   9.3         1\n",
       "48     2.6       53        66  10.8         1\n",
       "49     6.8      161        60  15.6         1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding clusters to dataset\n",
    "crime['clusters']=dbscan.labels_\n",
    "crime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>clusters</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1</td>\n",
       "      <td>11.005556</td>\n",
       "      <td>247.166667</td>\n",
       "      <td>70.666667</td>\n",
       "      <td>28.766667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>14.050000</td>\n",
       "      <td>238.000000</td>\n",
       "      <td>57.750000</td>\n",
       "      <td>23.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>4.825000</td>\n",
       "      <td>112.035714</td>\n",
       "      <td>63.357143</td>\n",
       "      <td>16.107143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  clusters     Murder     Assault   UrbanPop       Rape\n",
       "                 mean        mean       mean       mean\n",
       "0       -1  11.005556  247.166667  70.666667  28.766667\n",
       "1        0  14.050000  238.000000  57.750000  23.200000\n",
       "2        1   4.825000  112.035714  63.357143  16.107143"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime.groupby('clusters').agg(['mean']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2b348c8af70>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Clusters\n",
    "plt.figure(figsize=(10, 7))  \n",
    "plt.scatter(crime['clusters'],crime['UrbanPop'], c=dbscan.labels_) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}