Crime_data Assignment 7.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b6cadf25",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e50c2a83",
   "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>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</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>Alaska</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>Arizona</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>Arkansas</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>California</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>Colorado</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>Connecticut</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>Delaware</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>Florida</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>Georgia</th>\n",
       "      <td>17.4</td>\n",
       "      <td>211</td>\n",
       "      <td>60</td>\n",
       "      <td>25.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Murder  Assault  UrbanPop  Rape\n",
       "Alabama        13.2      236        58  21.2\n",
       "Alaska         10.0      263        48  44.5\n",
       "Arizona         8.1      294        80  31.0\n",
       "Arkansas        8.8      190        50  19.5\n",
       "California      9.0      276        91  40.6\n",
       "Colorado        7.9      204        78  38.7\n",
       "Connecticut     3.3      110        77  11.1\n",
       "Delaware        5.9      238        72  15.8\n",
       "Florida        15.4      335        80  31.9\n",
       "Georgia        17.4      211        60  25.8"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime=pd.read_csv(\"C:/Users/mewit/Desktop/Data Science Notes/Assignmnets/7 - Clustering/crime_data.csv\",index_col=0)\n",
    "crime.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "186417ce",
   "metadata": {},
   "source": [
    "# EDA Process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6d8049ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 50 entries, Alabama to Wyoming\n",
      "Data columns (total 4 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Murder    50 non-null     float64\n",
      " 1   Assault   50 non-null     int64  \n",
      " 2   UrbanPop  50 non-null     int64  \n",
      " 3   Rape      50 non-null     float64\n",
      "dtypes: float64(2), int64(2)\n",
      "memory usage: 2.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# Checking the data types\n",
    "crime.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "553a40ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Murder      0\n",
       "Assault     0\n",
       "UrbanPop    0\n",
       "Rape        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the null values\n",
    "crime.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f619fb8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for the duplicates crime location\n",
    "crime.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6b80a70c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "50963f4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.74698795 0.65410959 0.44067797 0.35917313]\n",
      " [0.55421687 0.74657534 0.27118644 0.96124031]\n",
      " [0.43975904 0.85273973 0.81355932 0.6124031 ]\n",
      " [0.48192771 0.49657534 0.30508475 0.31524548]\n",
      " [0.4939759  0.79109589 1.         0.86046512]]\n"
     ]
    }
   ],
   "source": [
    "# Normalising the data\n",
    "scaler=MinMaxScaler(feature_range=(0,1))\n",
    "crime_N=scaler.fit_transform(crime)\n",
    "print(crime_N[0:5,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d823be10",
   "metadata": {},
   "source": [
    "# Hierarchichal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a778e14",
   "metadata": {},
   "source": [
    "### Creating Dendrogram and Cluster "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2711ef5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.cluster.hierarchy as sch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f726b7c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For making dendrogram with center points\n",
    "plt.figure(figsize=(12,6))\n",
    "fig=sch.dendrogram(sch.linkage(crime,method='average'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "02f73542",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "545d0889",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Clusters\n",
    "cluster = AgglomerativeClustering(n_clusters=5, affinity = 'euclidean', linkage = 'complete')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7cd26530",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 4, 1, 0, 1, 1, 0, 0, 1, 3, 0, 2, 1, 0, 2, 0, 0, 3, 2, 1, 0, 1,\n",
       "       0, 3, 1, 2, 2, 1, 2, 0, 1, 1, 3, 2, 0, 0, 0, 0, 0, 3, 2, 3, 1, 0,\n",
       "       2, 0, 0, 2, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clst = cluster.fit_predict(crime_N)\n",
    "clst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "75fdc319",
   "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>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "      <th>cluster_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>13.2</td>\n",
       "      <td>236</td>\n",
       "      <td>58</td>\n",
       "      <td>21.2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>10.0</td>\n",
       "      <td>263</td>\n",
       "      <td>48</td>\n",
       "      <td>44.5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>8.1</td>\n",
       "      <td>294</td>\n",
       "      <td>80</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>8.8</td>\n",
       "      <td>190</td>\n",
       "      <td>50</td>\n",
       "      <td>19.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>9.0</td>\n",
       "      <td>276</td>\n",
       "      <td>91</td>\n",
       "      <td>40.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>7.9</td>\n",
       "      <td>204</td>\n",
       "      <td>78</td>\n",
       "      <td>38.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>3.3</td>\n",
       "      <td>110</td>\n",
       "      <td>77</td>\n",
       "      <td>11.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delaware</th>\n",
       "      <td>5.9</td>\n",
       "      <td>238</td>\n",
       "      <td>72</td>\n",
       "      <td>15.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</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>Georgia</th>\n",
       "      <td>17.4</td>\n",
       "      <td>211</td>\n",
       "      <td>60</td>\n",
       "      <td>25.8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hawaii</th>\n",
       "      <td>5.3</td>\n",
       "      <td>46</td>\n",
       "      <td>83</td>\n",
       "      <td>20.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idaho</th>\n",
       "      <td>2.6</td>\n",
       "      <td>120</td>\n",
       "      <td>54</td>\n",
       "      <td>14.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</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>Indiana</th>\n",
       "      <td>7.2</td>\n",
       "      <td>113</td>\n",
       "      <td>65</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iowa</th>\n",
       "      <td>2.2</td>\n",
       "      <td>56</td>\n",
       "      <td>57</td>\n",
       "      <td>11.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kansas</th>\n",
       "      <td>6.0</td>\n",
       "      <td>115</td>\n",
       "      <td>66</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kentucky</th>\n",
       "      <td>9.7</td>\n",
       "      <td>109</td>\n",
       "      <td>52</td>\n",
       "      <td>16.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Louisiana</th>\n",
       "      <td>15.4</td>\n",
       "      <td>249</td>\n",
       "      <td>66</td>\n",
       "      <td>22.2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>2.1</td>\n",
       "      <td>83</td>\n",
       "      <td>51</td>\n",
       "      <td>7.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maryland</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>Massachusetts</th>\n",
       "      <td>4.4</td>\n",
       "      <td>149</td>\n",
       "      <td>85</td>\n",
       "      <td>16.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan</th>\n",
       "      <td>12.1</td>\n",
       "      <td>255</td>\n",
       "      <td>74</td>\n",
       "      <td>35.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minnesota</th>\n",
       "      <td>2.7</td>\n",
       "      <td>72</td>\n",
       "      <td>66</td>\n",
       "      <td>14.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mississippi</th>\n",
       "      <td>16.1</td>\n",
       "      <td>259</td>\n",
       "      <td>44</td>\n",
       "      <td>17.1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missouri</th>\n",
       "      <td>9.0</td>\n",
       "      <td>178</td>\n",
       "      <td>70</td>\n",
       "      <td>28.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montana</th>\n",
       "      <td>6.0</td>\n",
       "      <td>109</td>\n",
       "      <td>53</td>\n",
       "      <td>16.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nebraska</th>\n",
       "      <td>4.3</td>\n",
       "      <td>102</td>\n",
       "      <td>62</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nevada</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>New Hampshire</th>\n",
       "      <td>2.1</td>\n",
       "      <td>57</td>\n",
       "      <td>56</td>\n",
       "      <td>9.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Jersey</th>\n",
       "      <td>7.4</td>\n",
       "      <td>159</td>\n",
       "      <td>89</td>\n",
       "      <td>18.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Mexico</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>New York</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>North Carolina</th>\n",
       "      <td>13.0</td>\n",
       "      <td>337</td>\n",
       "      <td>45</td>\n",
       "      <td>16.1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Dakota</th>\n",
       "      <td>0.8</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>7.3</td>\n",
       "      <td>120</td>\n",
       "      <td>75</td>\n",
       "      <td>21.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oklahoma</th>\n",
       "      <td>6.6</td>\n",
       "      <td>151</td>\n",
       "      <td>68</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>4.9</td>\n",
       "      <td>159</td>\n",
       "      <td>67</td>\n",
       "      <td>29.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pennsylvania</th>\n",
       "      <td>6.3</td>\n",
       "      <td>106</td>\n",
       "      <td>72</td>\n",
       "      <td>14.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rhode Island</th>\n",
       "      <td>3.4</td>\n",
       "      <td>174</td>\n",
       "      <td>87</td>\n",
       "      <td>8.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Carolina</th>\n",
       "      <td>14.4</td>\n",
       "      <td>279</td>\n",
       "      <td>48</td>\n",
       "      <td>22.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Dakota</th>\n",
       "      <td>3.8</td>\n",
       "      <td>86</td>\n",
       "      <td>45</td>\n",
       "      <td>12.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tennessee</th>\n",
       "      <td>13.2</td>\n",
       "      <td>188</td>\n",
       "      <td>59</td>\n",
       "      <td>26.9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</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>Utah</th>\n",
       "      <td>3.2</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vermont</th>\n",
       "      <td>2.2</td>\n",
       "      <td>48</td>\n",
       "      <td>32</td>\n",
       "      <td>11.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virginia</th>\n",
       "      <td>8.5</td>\n",
       "      <td>156</td>\n",
       "      <td>63</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington</th>\n",
       "      <td>4.0</td>\n",
       "      <td>145</td>\n",
       "      <td>73</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Virginia</th>\n",
       "      <td>5.7</td>\n",
       "      <td>81</td>\n",
       "      <td>39</td>\n",
       "      <td>9.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wisconsin</th>\n",
       "      <td>2.6</td>\n",
       "      <td>53</td>\n",
       "      <td>66</td>\n",
       "      <td>10.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>6.8</td>\n",
       "      <td>161</td>\n",
       "      <td>60</td>\n",
       "      <td>15.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Murder  Assault  UrbanPop  Rape  cluster_id\n",
       "Alabama           13.2      236        58  21.2           3\n",
       "Alaska            10.0      263        48  44.5           4\n",
       "Arizona            8.1      294        80  31.0           1\n",
       "Arkansas           8.8      190        50  19.5           0\n",
       "California         9.0      276        91  40.6           1\n",
       "Colorado           7.9      204        78  38.7           1\n",
       "Connecticut        3.3      110        77  11.1           0\n",
       "Delaware           5.9      238        72  15.8           0\n",
       "Florida           15.4      335        80  31.9           1\n",
       "Georgia           17.4      211        60  25.8           3\n",
       "Hawaii             5.3       46        83  20.2           0\n",
       "Idaho              2.6      120        54  14.2           2\n",
       "Illinois          10.4      249        83  24.0           1\n",
       "Indiana            7.2      113        65  21.0           0\n",
       "Iowa               2.2       56        57  11.3           2\n",
       "Kansas             6.0      115        66  18.0           0\n",
       "Kentucky           9.7      109        52  16.3           0\n",
       "Louisiana         15.4      249        66  22.2           3\n",
       "Maine              2.1       83        51   7.8           2\n",
       "Maryland          11.3      300        67  27.8           1\n",
       "Massachusetts      4.4      149        85  16.3           0\n",
       "Michigan          12.1      255        74  35.1           1\n",
       "Minnesota          2.7       72        66  14.9           0\n",
       "Mississippi       16.1      259        44  17.1           3\n",
       "Missouri           9.0      178        70  28.2           1\n",
       "Montana            6.0      109        53  16.4           2\n",
       "Nebraska           4.3      102        62  16.5           2\n",
       "Nevada            12.2      252        81  46.0           1\n",
       "New Hampshire      2.1       57        56   9.5           2\n",
       "New Jersey         7.4      159        89  18.8           0\n",
       "New Mexico        11.4      285        70  32.1           1\n",
       "New York          11.1      254        86  26.1           1\n",
       "North Carolina    13.0      337        45  16.1           3\n",
       "North Dakota       0.8       45        44   7.3           2\n",
       "Ohio               7.3      120        75  21.4           0\n",
       "Oklahoma           6.6      151        68  20.0           0\n",
       "Oregon             4.9      159        67  29.3           0\n",
       "Pennsylvania       6.3      106        72  14.9           0\n",
       "Rhode Island       3.4      174        87   8.3           0\n",
       "South Carolina    14.4      279        48  22.5           3\n",
       "South Dakota       3.8       86        45  12.8           2\n",
       "Tennessee         13.2      188        59  26.9           3\n",
       "Texas             12.7      201        80  25.5           1\n",
       "Utah               3.2      120        80  22.9           0\n",
       "Vermont            2.2       48        32  11.2           2\n",
       "Virginia           8.5      156        63  20.7           0\n",
       "Washington         4.0      145        73  26.2           0\n",
       "West Virginia      5.7       81        39   9.3           2\n",
       "Wisconsin          2.6       53        66  10.8           0\n",
       "Wyoming            6.8      161        60  15.6           0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime1=crime.copy()\n",
    "crime1['cluster_id'] = clst\n",
    "crime1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "06fa4daa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    20\n",
       "1    12\n",
       "2    10\n",
       "3     7\n",
       "4     1\n",
       "Name: cluster_id, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime1['cluster_id'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb01e5f7",
   "metadata": {},
   "source": [
    "# K Means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "85194272",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "461dd74e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# within-cluster sum-of-squares criterion \n",
    "wcss=[]\n",
    "for i in range (1,11):\n",
    "    kmeans=KMeans(n_clusters=i,random_state=2)\n",
    "    kmeans.fit(crime_N)\n",
    "    wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b141dbd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot K values range vs WCSS to get Elbow graph for choosing K (no. of clusters)\n",
    "plt.plot(range(1,11),wcss)\n",
    "plt.title('Elbow Graph')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e4a6017",
   "metadata": {},
   "source": [
    "### Build Cluster algorithm using K=4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "338f13da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=4, random_state=30)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters4=KMeans(4,random_state=30).fit(crime_N)\n",
    "clusters4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6097f45b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 2, 2, 3, 2, 2, 1, 1, 2, 3, 1, 0, 2, 1, 0, 1, 0, 3, 0, 2, 1, 2,\n",
       "       0, 3, 2, 0, 0, 2, 0, 1, 2, 2, 3, 0, 1, 1, 1, 1, 1, 3, 0, 3, 2, 1,\n",
       "       0, 1, 1, 0, 0, 1])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters4.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2b4b2a6e",
   "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>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "      <th>clusters4_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>13.2</td>\n",
       "      <td>236</td>\n",
       "      <td>58</td>\n",
       "      <td>21.2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>10.0</td>\n",
       "      <td>263</td>\n",
       "      <td>48</td>\n",
       "      <td>44.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>8.1</td>\n",
       "      <td>294</td>\n",
       "      <td>80</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>8.8</td>\n",
       "      <td>190</td>\n",
       "      <td>50</td>\n",
       "      <td>19.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>9.0</td>\n",
       "      <td>276</td>\n",
       "      <td>91</td>\n",
       "      <td>40.6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>7.9</td>\n",
       "      <td>204</td>\n",
       "      <td>78</td>\n",
       "      <td>38.7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>3.3</td>\n",
       "      <td>110</td>\n",
       "      <td>77</td>\n",
       "      <td>11.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delaware</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>Florida</th>\n",
       "      <td>15.4</td>\n",
       "      <td>335</td>\n",
       "      <td>80</td>\n",
       "      <td>31.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>17.4</td>\n",
       "      <td>211</td>\n",
       "      <td>60</td>\n",
       "      <td>25.8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hawaii</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>Idaho</th>\n",
       "      <td>2.6</td>\n",
       "      <td>120</td>\n",
       "      <td>54</td>\n",
       "      <td>14.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>10.4</td>\n",
       "      <td>249</td>\n",
       "      <td>83</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiana</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>Iowa</th>\n",
       "      <td>2.2</td>\n",
       "      <td>56</td>\n",
       "      <td>57</td>\n",
       "      <td>11.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kansas</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>Kentucky</th>\n",
       "      <td>9.7</td>\n",
       "      <td>109</td>\n",
       "      <td>52</td>\n",
       "      <td>16.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Louisiana</th>\n",
       "      <td>15.4</td>\n",
       "      <td>249</td>\n",
       "      <td>66</td>\n",
       "      <td>22.2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>2.1</td>\n",
       "      <td>83</td>\n",
       "      <td>51</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maryland</th>\n",
       "      <td>11.3</td>\n",
       "      <td>300</td>\n",
       "      <td>67</td>\n",
       "      <td>27.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Massachusetts</th>\n",
       "      <td>4.4</td>\n",
       "      <td>149</td>\n",
       "      <td>85</td>\n",
       "      <td>16.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan</th>\n",
       "      <td>12.1</td>\n",
       "      <td>255</td>\n",
       "      <td>74</td>\n",
       "      <td>35.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minnesota</th>\n",
       "      <td>2.7</td>\n",
       "      <td>72</td>\n",
       "      <td>66</td>\n",
       "      <td>14.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mississippi</th>\n",
       "      <td>16.1</td>\n",
       "      <td>259</td>\n",
       "      <td>44</td>\n",
       "      <td>17.1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missouri</th>\n",
       "      <td>9.0</td>\n",
       "      <td>178</td>\n",
       "      <td>70</td>\n",
       "      <td>28.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montana</th>\n",
       "      <td>6.0</td>\n",
       "      <td>109</td>\n",
       "      <td>53</td>\n",
       "      <td>16.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nebraska</th>\n",
       "      <td>4.3</td>\n",
       "      <td>102</td>\n",
       "      <td>62</td>\n",
       "      <td>16.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nevada</th>\n",
       "      <td>12.2</td>\n",
       "      <td>252</td>\n",
       "      <td>81</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Hampshire</th>\n",
       "      <td>2.1</td>\n",
       "      <td>57</td>\n",
       "      <td>56</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Jersey</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>New Mexico</th>\n",
       "      <td>11.4</td>\n",
       "      <td>285</td>\n",
       "      <td>70</td>\n",
       "      <td>32.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>11.1</td>\n",
       "      <td>254</td>\n",
       "      <td>86</td>\n",
       "      <td>26.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Carolina</th>\n",
       "      <td>13.0</td>\n",
       "      <td>337</td>\n",
       "      <td>45</td>\n",
       "      <td>16.1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Dakota</th>\n",
       "      <td>0.8</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</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>Oklahoma</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>Oregon</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>Pennsylvania</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>Rhode Island</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>South Carolina</th>\n",
       "      <td>14.4</td>\n",
       "      <td>279</td>\n",
       "      <td>48</td>\n",
       "      <td>22.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Dakota</th>\n",
       "      <td>3.8</td>\n",
       "      <td>86</td>\n",
       "      <td>45</td>\n",
       "      <td>12.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tennessee</th>\n",
       "      <td>13.2</td>\n",
       "      <td>188</td>\n",
       "      <td>59</td>\n",
       "      <td>26.9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>12.7</td>\n",
       "      <td>201</td>\n",
       "      <td>80</td>\n",
       "      <td>25.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</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>Vermont</th>\n",
       "      <td>2.2</td>\n",
       "      <td>48</td>\n",
       "      <td>32</td>\n",
       "      <td>11.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virginia</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>Washington</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>West Virginia</th>\n",
       "      <td>5.7</td>\n",
       "      <td>81</td>\n",
       "      <td>39</td>\n",
       "      <td>9.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wisconsin</th>\n",
       "      <td>2.6</td>\n",
       "      <td>53</td>\n",
       "      <td>66</td>\n",
       "      <td>10.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</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  clusters4_id\n",
       "Alabama           13.2      236        58  21.2             3\n",
       "Alaska            10.0      263        48  44.5             2\n",
       "Arizona            8.1      294        80  31.0             2\n",
       "Arkansas           8.8      190        50  19.5             3\n",
       "California         9.0      276        91  40.6             2\n",
       "Colorado           7.9      204        78  38.7             2\n",
       "Connecticut        3.3      110        77  11.1             1\n",
       "Delaware           5.9      238        72  15.8             1\n",
       "Florida           15.4      335        80  31.9             2\n",
       "Georgia           17.4      211        60  25.8             3\n",
       "Hawaii             5.3       46        83  20.2             1\n",
       "Idaho              2.6      120        54  14.2             0\n",
       "Illinois          10.4      249        83  24.0             2\n",
       "Indiana            7.2      113        65  21.0             1\n",
       "Iowa               2.2       56        57  11.3             0\n",
       "Kansas             6.0      115        66  18.0             1\n",
       "Kentucky           9.7      109        52  16.3             0\n",
       "Louisiana         15.4      249        66  22.2             3\n",
       "Maine              2.1       83        51   7.8             0\n",
       "Maryland          11.3      300        67  27.8             2\n",
       "Massachusetts      4.4      149        85  16.3             1\n",
       "Michigan          12.1      255        74  35.1             2\n",
       "Minnesota          2.7       72        66  14.9             0\n",
       "Mississippi       16.1      259        44  17.1             3\n",
       "Missouri           9.0      178        70  28.2             2\n",
       "Montana            6.0      109        53  16.4             0\n",
       "Nebraska           4.3      102        62  16.5             0\n",
       "Nevada            12.2      252        81  46.0             2\n",
       "New Hampshire      2.1       57        56   9.5             0\n",
       "New Jersey         7.4      159        89  18.8             1\n",
       "New Mexico        11.4      285        70  32.1             2\n",
       "New York          11.1      254        86  26.1             2\n",
       "North Carolina    13.0      337        45  16.1             3\n",
       "North Dakota       0.8       45        44   7.3             0\n",
       "Ohio               7.3      120        75  21.4             1\n",
       "Oklahoma           6.6      151        68  20.0             1\n",
       "Oregon             4.9      159        67  29.3             1\n",
       "Pennsylvania       6.3      106        72  14.9             1\n",
       "Rhode Island       3.4      174        87   8.3             1\n",
       "South Carolina    14.4      279        48  22.5             3\n",
       "South Dakota       3.8       86        45  12.8             0\n",
       "Tennessee         13.2      188        59  26.9             3\n",
       "Texas             12.7      201        80  25.5             2\n",
       "Utah               3.2      120        80  22.9             1\n",
       "Vermont            2.2       48        32  11.2             0\n",
       "Virginia           8.5      156        63  20.7             1\n",
       "Washington         4.0      145        73  26.2             1\n",
       "West Virginia      5.7       81        39   9.3             0\n",
       "Wisconsin          2.6       53        66  10.8             0\n",
       "Wyoming            6.8      161        60  15.6             1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assign clusters to the data set\n",
    "crime4=crime.copy()\n",
    "crime4['clusters4_id']=clusters4.labels_\n",
    "crime4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3c9645e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.1686747 , 0.11485774, 0.34028683, 0.12601868],\n",
       "       [0.29254518, 0.32148973, 0.70974576, 0.29667313],\n",
       "       [0.60333642, 0.72734457, 0.74576271, 0.66905188],\n",
       "       [0.79141566, 0.6802226 , 0.36864407, 0.36466408]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the centroids for K=4 clusters with 4 variables\n",
    "clusters4.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f24f084a",
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>clusters4_id</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>0</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>78.538462</td>\n",
       "      <td>52.076923</td>\n",
       "      <td>12.176923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5.656250</td>\n",
       "      <td>138.875000</td>\n",
       "      <td>73.875000</td>\n",
       "      <td>18.781250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>10.815385</td>\n",
       "      <td>257.384615</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>33.192308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>13.937500</td>\n",
       "      <td>243.625000</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>21.412500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  clusters4_id     Murder     Assault   UrbanPop       Rape\n",
       "                     mean        mean       mean       mean\n",
       "0            0   3.600000   78.538462  52.076923  12.176923\n",
       "1            1   5.656250  138.875000  73.875000  18.781250\n",
       "2            2  10.815385  257.384615  76.000000  33.192308\n",
       "3            3  13.937500  243.625000  53.750000  21.412500"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group data by Clusters (K=4)\n",
    "crime4.groupby('clusters4_id').agg(['mean']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5c6b9ce2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    16\n",
       "2    13\n",
       "0    13\n",
       "3     8\n",
       "Name: clusters4_id, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime4['clusters4_id'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72b37bed",
   "metadata": {},
   "source": [
    "### Build Cluster algorithm using K=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d167e43c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=4, random_state=30)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters5=KMeans(4,random_state=30).fit(crime_N)\n",
    "clusters5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9378ac0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 2, 2, 3, 2, 2, 1, 1, 2, 3, 1, 0, 2, 1, 0, 1, 0, 3, 0, 2, 1, 2,\n",
       "       0, 3, 2, 0, 0, 2, 0, 1, 2, 2, 3, 0, 1, 1, 1, 1, 1, 3, 0, 3, 2, 1,\n",
       "       0, 1, 1, 0, 0, 1])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters5.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dc47f8ec",
   "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>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "      <th>clusters5_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>13.2</td>\n",
       "      <td>236</td>\n",
       "      <td>58</td>\n",
       "      <td>21.2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>10.0</td>\n",
       "      <td>263</td>\n",
       "      <td>48</td>\n",
       "      <td>44.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>8.1</td>\n",
       "      <td>294</td>\n",
       "      <td>80</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>8.8</td>\n",
       "      <td>190</td>\n",
       "      <td>50</td>\n",
       "      <td>19.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>9.0</td>\n",
       "      <td>276</td>\n",
       "      <td>91</td>\n",
       "      <td>40.6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>7.9</td>\n",
       "      <td>204</td>\n",
       "      <td>78</td>\n",
       "      <td>38.7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>3.3</td>\n",
       "      <td>110</td>\n",
       "      <td>77</td>\n",
       "      <td>11.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delaware</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>Florida</th>\n",
       "      <td>15.4</td>\n",
       "      <td>335</td>\n",
       "      <td>80</td>\n",
       "      <td>31.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>17.4</td>\n",
       "      <td>211</td>\n",
       "      <td>60</td>\n",
       "      <td>25.8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hawaii</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>Idaho</th>\n",
       "      <td>2.6</td>\n",
       "      <td>120</td>\n",
       "      <td>54</td>\n",
       "      <td>14.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>10.4</td>\n",
       "      <td>249</td>\n",
       "      <td>83</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiana</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>Iowa</th>\n",
       "      <td>2.2</td>\n",
       "      <td>56</td>\n",
       "      <td>57</td>\n",
       "      <td>11.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kansas</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>Kentucky</th>\n",
       "      <td>9.7</td>\n",
       "      <td>109</td>\n",
       "      <td>52</td>\n",
       "      <td>16.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Louisiana</th>\n",
       "      <td>15.4</td>\n",
       "      <td>249</td>\n",
       "      <td>66</td>\n",
       "      <td>22.2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>2.1</td>\n",
       "      <td>83</td>\n",
       "      <td>51</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maryland</th>\n",
       "      <td>11.3</td>\n",
       "      <td>300</td>\n",
       "      <td>67</td>\n",
       "      <td>27.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Massachusetts</th>\n",
       "      <td>4.4</td>\n",
       "      <td>149</td>\n",
       "      <td>85</td>\n",
       "      <td>16.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan</th>\n",
       "      <td>12.1</td>\n",
       "      <td>255</td>\n",
       "      <td>74</td>\n",
       "      <td>35.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minnesota</th>\n",
       "      <td>2.7</td>\n",
       "      <td>72</td>\n",
       "      <td>66</td>\n",
       "      <td>14.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mississippi</th>\n",
       "      <td>16.1</td>\n",
       "      <td>259</td>\n",
       "      <td>44</td>\n",
       "      <td>17.1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missouri</th>\n",
       "      <td>9.0</td>\n",
       "      <td>178</td>\n",
       "      <td>70</td>\n",
       "      <td>28.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montana</th>\n",
       "      <td>6.0</td>\n",
       "      <td>109</td>\n",
       "      <td>53</td>\n",
       "      <td>16.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nebraska</th>\n",
       "      <td>4.3</td>\n",
       "      <td>102</td>\n",
       "      <td>62</td>\n",
       "      <td>16.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nevada</th>\n",
       "      <td>12.2</td>\n",
       "      <td>252</td>\n",
       "      <td>81</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Hampshire</th>\n",
       "      <td>2.1</td>\n",
       "      <td>57</td>\n",
       "      <td>56</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Jersey</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>New Mexico</th>\n",
       "      <td>11.4</td>\n",
       "      <td>285</td>\n",
       "      <td>70</td>\n",
       "      <td>32.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>11.1</td>\n",
       "      <td>254</td>\n",
       "      <td>86</td>\n",
       "      <td>26.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Carolina</th>\n",
       "      <td>13.0</td>\n",
       "      <td>337</td>\n",
       "      <td>45</td>\n",
       "      <td>16.1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Dakota</th>\n",
       "      <td>0.8</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</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>Oklahoma</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>Oregon</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>Pennsylvania</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>Rhode Island</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>South Carolina</th>\n",
       "      <td>14.4</td>\n",
       "      <td>279</td>\n",
       "      <td>48</td>\n",
       "      <td>22.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Dakota</th>\n",
       "      <td>3.8</td>\n",
       "      <td>86</td>\n",
       "      <td>45</td>\n",
       "      <td>12.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tennessee</th>\n",
       "      <td>13.2</td>\n",
       "      <td>188</td>\n",
       "      <td>59</td>\n",
       "      <td>26.9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>12.7</td>\n",
       "      <td>201</td>\n",
       "      <td>80</td>\n",
       "      <td>25.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</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>Vermont</th>\n",
       "      <td>2.2</td>\n",
       "      <td>48</td>\n",
       "      <td>32</td>\n",
       "      <td>11.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virginia</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>Washington</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>West Virginia</th>\n",
       "      <td>5.7</td>\n",
       "      <td>81</td>\n",
       "      <td>39</td>\n",
       "      <td>9.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wisconsin</th>\n",
       "      <td>2.6</td>\n",
       "      <td>53</td>\n",
       "      <td>66</td>\n",
       "      <td>10.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</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  clusters5_id\n",
       "Alabama           13.2      236        58  21.2             3\n",
       "Alaska            10.0      263        48  44.5             2\n",
       "Arizona            8.1      294        80  31.0             2\n",
       "Arkansas           8.8      190        50  19.5             3\n",
       "California         9.0      276        91  40.6             2\n",
       "Colorado           7.9      204        78  38.7             2\n",
       "Connecticut        3.3      110        77  11.1             1\n",
       "Delaware           5.9      238        72  15.8             1\n",
       "Florida           15.4      335        80  31.9             2\n",
       "Georgia           17.4      211        60  25.8             3\n",
       "Hawaii             5.3       46        83  20.2             1\n",
       "Idaho              2.6      120        54  14.2             0\n",
       "Illinois          10.4      249        83  24.0             2\n",
       "Indiana            7.2      113        65  21.0             1\n",
       "Iowa               2.2       56        57  11.3             0\n",
       "Kansas             6.0      115        66  18.0             1\n",
       "Kentucky           9.7      109        52  16.3             0\n",
       "Louisiana         15.4      249        66  22.2             3\n",
       "Maine              2.1       83        51   7.8             0\n",
       "Maryland          11.3      300        67  27.8             2\n",
       "Massachusetts      4.4      149        85  16.3             1\n",
       "Michigan          12.1      255        74  35.1             2\n",
       "Minnesota          2.7       72        66  14.9             0\n",
       "Mississippi       16.1      259        44  17.1             3\n",
       "Missouri           9.0      178        70  28.2             2\n",
       "Montana            6.0      109        53  16.4             0\n",
       "Nebraska           4.3      102        62  16.5             0\n",
       "Nevada            12.2      252        81  46.0             2\n",
       "New Hampshire      2.1       57        56   9.5             0\n",
       "New Jersey         7.4      159        89  18.8             1\n",
       "New Mexico        11.4      285        70  32.1             2\n",
       "New York          11.1      254        86  26.1             2\n",
       "North Carolina    13.0      337        45  16.1             3\n",
       "North Dakota       0.8       45        44   7.3             0\n",
       "Ohio               7.3      120        75  21.4             1\n",
       "Oklahoma           6.6      151        68  20.0             1\n",
       "Oregon             4.9      159        67  29.3             1\n",
       "Pennsylvania       6.3      106        72  14.9             1\n",
       "Rhode Island       3.4      174        87   8.3             1\n",
       "South Carolina    14.4      279        48  22.5             3\n",
       "South Dakota       3.8       86        45  12.8             0\n",
       "Tennessee         13.2      188        59  26.9             3\n",
       "Texas             12.7      201        80  25.5             2\n",
       "Utah               3.2      120        80  22.9             1\n",
       "Vermont            2.2       48        32  11.2             0\n",
       "Virginia           8.5      156        63  20.7             1\n",
       "Washington         4.0      145        73  26.2             1\n",
       "West Virginia      5.7       81        39   9.3             0\n",
       "Wisconsin          2.6       53        66  10.8             0\n",
       "Wyoming            6.8      161        60  15.6             1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assign clusters to the data set\n",
    "crime5=crime.copy()\n",
    "crime5['clusters5_id']=clusters5.labels_\n",
    "crime5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c1cba3be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.1686747 , 0.11485774, 0.34028683, 0.12601868],\n",
       "       [0.29254518, 0.32148973, 0.70974576, 0.29667313],\n",
       "       [0.60333642, 0.72734457, 0.74576271, 0.66905188],\n",
       "       [0.79141566, 0.6802226 , 0.36864407, 0.36466408]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the centroids for K=5 clusters with 4 variables\n",
    "clusters5.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5997e53a",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>clusters5_id</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>0</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>78.538462</td>\n",
       "      <td>52.076923</td>\n",
       "      <td>12.176923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5.656250</td>\n",
       "      <td>138.875000</td>\n",
       "      <td>73.875000</td>\n",
       "      <td>18.781250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>10.815385</td>\n",
       "      <td>257.384615</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>33.192308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>13.937500</td>\n",
       "      <td>243.625000</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>21.412500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  clusters5_id     Murder     Assault   UrbanPop       Rape\n",
       "                     mean        mean       mean       mean\n",
       "0            0   3.600000   78.538462  52.076923  12.176923\n",
       "1            1   5.656250  138.875000  73.875000  18.781250\n",
       "2            2  10.815385  257.384615  76.000000  33.192308\n",
       "3            3  13.937500  243.625000  53.750000  21.412500"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group data by Clusters (K=4)\n",
    "crime5.groupby('clusters5_id').agg(['mean']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2ffc78f7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    16\n",
       "2    13\n",
       "0    13\n",
       "3     8\n",
       "Name: clusters5_id, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime5['clusters5_id'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82ee364a",
   "metadata": {},
   "source": [
    "# DBSCAN Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b950d32d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "37b80743",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler().fit(crime)\n",
    "crime_norm = scaler.transform(crime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4821ceb3",
   "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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3cfb7bfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DBSCAN(eps=0.98, min_samples=3)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DBSCAN Clustering\n",
    "dbscan = DBSCAN(eps=0.98, min_samples=3)\n",
    "dbscan.fit(crime_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "33568b93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0, -1, -1, -1, -1, -1,  1, -1, -1,  0, -1,  1,  2,  1,  1,  1,  1,\n",
       "        0,  1,  3,  1,  3,  1,  0, -1,  1,  1, -1,  1,  1,  3,  2, -1,  1,\n",
       "        1,  1,  1,  1,  1,  0,  1,  0,  2,  1, -1,  1,  1,  1,  1,  1],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Noisy samples are given the label -1.\n",
    "dbscan.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "61872010",
   "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>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</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>Alaska</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>Arizona</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>Arkansas</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>California</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>Colorado</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>Connecticut</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>Delaware</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>Florida</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>Georgia</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>Hawaii</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>Idaho</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>Illinois</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>Indiana</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>Iowa</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>Kansas</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>Kentucky</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>Louisiana</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>Maine</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>Maryland</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>Massachusetts</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>Michigan</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>Minnesota</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>Mississippi</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>Missouri</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>Montana</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>Nebraska</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>Nevada</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>New Hampshire</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>New Jersey</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>New Mexico</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>New York</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>North Carolina</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>North Dakota</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>Ohio</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>Oklahoma</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>Oregon</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>Pennsylvania</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>Rhode Island</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>South Carolina</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>South Dakota</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>Tennessee</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>Texas</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>Utah</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>Vermont</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>Virginia</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>Washington</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>West Virginia</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>Wisconsin</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>Wyoming</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",
       "Alabama           13.2      236        58  21.2\n",
       "Alaska            10.0      263        48  44.5\n",
       "Arizona            8.1      294        80  31.0\n",
       "Arkansas           8.8      190        50  19.5\n",
       "California         9.0      276        91  40.6\n",
       "Colorado           7.9      204        78  38.7\n",
       "Connecticut        3.3      110        77  11.1\n",
       "Delaware           5.9      238        72  15.8\n",
       "Florida           15.4      335        80  31.9\n",
       "Georgia           17.4      211        60  25.8\n",
       "Hawaii             5.3       46        83  20.2\n",
       "Idaho              2.6      120        54  14.2\n",
       "Illinois          10.4      249        83  24.0\n",
       "Indiana            7.2      113        65  21.0\n",
       "Iowa               2.2       56        57  11.3\n",
       "Kansas             6.0      115        66  18.0\n",
       "Kentucky           9.7      109        52  16.3\n",
       "Louisiana         15.4      249        66  22.2\n",
       "Maine              2.1       83        51   7.8\n",
       "Maryland          11.3      300        67  27.8\n",
       "Massachusetts      4.4      149        85  16.3\n",
       "Michigan          12.1      255        74  35.1\n",
       "Minnesota          2.7       72        66  14.9\n",
       "Mississippi       16.1      259        44  17.1\n",
       "Missouri           9.0      178        70  28.2\n",
       "Montana            6.0      109        53  16.4\n",
       "Nebraska           4.3      102        62  16.5\n",
       "Nevada            12.2      252        81  46.0\n",
       "New Hampshire      2.1       57        56   9.5\n",
       "New Jersey         7.4      159        89  18.8\n",
       "New Mexico        11.4      285        70  32.1\n",
       "New York          11.1      254        86  26.1\n",
       "North Carolina    13.0      337        45  16.1\n",
       "North Dakota       0.8       45        44   7.3\n",
       "Ohio               7.3      120        75  21.4\n",
       "Oklahoma           6.6      151        68  20.0\n",
       "Oregon             4.9      159        67  29.3\n",
       "Pennsylvania       6.3      106        72  14.9\n",
       "Rhode Island       3.4      174        87   8.3\n",
       "South Carolina    14.4      279        48  22.5\n",
       "South Dakota       3.8       86        45  12.8\n",
       "Tennessee         13.2      188        59  26.9\n",
       "Texas             12.7      201        80  25.5\n",
       "Utah               3.2      120        80  22.9\n",
       "Vermont            2.2       48        32  11.2\n",
       "Virginia           8.5      156        63  20.7\n",
       "Washington         4.0      145        73  26.2\n",
       "West Virginia      5.7       81        39   9.3\n",
       "Wisconsin          2.6       53        66  10.8\n",
       "Wyoming            6.8      161        60  15.6"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ab65343a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cluster_id</th>\n",
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       "  </thead>\n",
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       "      <th>13</th>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
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       "      <th>17</th>\n",
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       "      <th>20</th>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
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       "      <th>24</th>\n",
       "      <td>-1</td>\n",
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       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3</td>\n",
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       "      <th>31</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>-1</td>\n",
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       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1</td>\n",
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       "      <th>41</th>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>-1</td>\n",
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       "      <th>45</th>\n",
       "      <td>1</td>\n",
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       "      <th>47</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    cluster_id\n",
       "0            0\n",
       "1           -1\n",
       "2           -1\n",
       "3           -1\n",
       "4           -1\n",
       "5           -1\n",
       "6            1\n",
       "7           -1\n",
       "8           -1\n",
       "9            0\n",
       "10          -1\n",
       "11           1\n",
       "12           2\n",
       "13           1\n",
       "14           1\n",
       "15           1\n",
       "16           1\n",
       "17           0\n",
       "18           1\n",
       "19           3\n",
       "20           1\n",
       "21           3\n",
       "22           1\n",
       "23           0\n",
       "24          -1\n",
       "25           1\n",
       "26           1\n",
       "27          -1\n",
       "28           1\n",
       "29           1\n",
       "30           3\n",
       "31           2\n",
       "32          -1\n",
       "33           1\n",
       "34           1\n",
       "35           1\n",
       "36           1\n",
       "37           1\n",
       "38           1\n",
       "39           0\n",
       "40           1\n",
       "41           0\n",
       "42           2\n",
       "43           1\n",
       "44          -1\n",
       "45           1\n",
       "46           1\n",
       "47           1\n",
       "48           1\n",
       "49           1"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1 = pd.DataFrame(dbscan.labels_,columns=['cluster_id'])\n",
    "a1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a1fe8758",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1.0    26\n",
       "-1.0    12\n",
       " 0.0     6\n",
       " 2.0     3\n",
       " 3.0     3\n",
       "Name: cluster_id, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2 = pd.concat([crime,a1],axis=1)\n",
    "a2['cluster_id'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2956387",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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