CLUSTERING_CRIME.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# we can infer that the third cluster or cluster two have more members followed by cluster zero or first cluster and cluster one have the least number of members"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.cluster.hierarchy as sch\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sn\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = pd.read_csv('crime_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>13.2</td>\n",
       "      <td>236</td>\n",
       "      <td>58</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>10.0</td>\n",
       "      <td>263</td>\n",
       "      <td>48</td>\n",
       "      <td>44.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>8.1</td>\n",
       "      <td>294</td>\n",
       "      <td>80</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>8.8</td>\n",
       "      <td>190</td>\n",
       "      <td>50</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>9.0</td>\n",
       "      <td>276</td>\n",
       "      <td>91</td>\n",
       "      <td>40.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Murder  Assault  UrbanPop  Rape\n",
       "0     Alabama    13.2      236        58  21.2\n",
       "1      Alaska    10.0      263        48  44.5\n",
       "2     Arizona     8.1      294        80  31.0\n",
       "3    Arkansas     8.8      190        50  19.5\n",
       "4  California     9.0      276        91  40.6"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50 entries, 0 to 49\n",
      "Data columns (total 5 columns):\n",
      "Unnamed: 0    50 non-null object\n",
      "Murder        50 non-null float64\n",
      "Assault       50 non-null int64\n",
      "UrbanPop      50 non-null int64\n",
      "Rape          50 non-null float64\n",
      "dtypes: float64(2), int64(2), object(1)\n",
      "memory usage: 2.0+ KB\n"
     ]
    }
   ],
   "source": [
    "data1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = data1.rename({'Unnamed: 0':'unnamed'},axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unnamed</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>13.2</td>\n",
       "      <td>236</td>\n",
       "      <td>58</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>10.0</td>\n",
       "      <td>263</td>\n",
       "      <td>48</td>\n",
       "      <td>44.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>8.1</td>\n",
       "      <td>294</td>\n",
       "      <td>80</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>8.8</td>\n",
       "      <td>190</td>\n",
       "      <td>50</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>9.0</td>\n",
       "      <td>276</td>\n",
       "      <td>91</td>\n",
       "      <td>40.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      unnamed  Murder  Assault  UrbanPop  Rape\n",
       "0     Alabama    13.2      236        58  21.2\n",
       "1      Alaska    10.0      263        48  44.5\n",
       "2     Arizona     8.1      294        80  31.0\n",
       "3    Arkansas     8.8      190        50  19.5\n",
       "4  California     9.0      276        91  40.6"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = data1.drop(['unnamed'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.2</td>\n",
       "      <td>236</td>\n",
       "      <td>58</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.0</td>\n",
       "      <td>263</td>\n",
       "      <td>48</td>\n",
       "      <td>44.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>294</td>\n",
       "      <td>80</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.8</td>\n",
       "      <td>190</td>\n",
       "      <td>50</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.0</td>\n",
       "      <td>276</td>\n",
       "      <td>91</td>\n",
       "      <td>40.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Murder  Assault  UrbanPop  Rape\n",
       "0    13.2      236        58  21.2\n",
       "1    10.0      263        48  44.5\n",
       "2     8.1      294        80  31.0\n",
       "3     8.8      190        50  19.5\n",
       "4     9.0      276        91  40.6"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hierarchical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalization function \n",
    "def norm_func(i):\n",
    "    x = (i-i.min())/(i.max()-i.min())\n",
    "    return (x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_norm = norm_func(data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.746988</td>\n",
       "      <td>0.654110</td>\n",
       "      <td>0.440678</td>\n",
       "      <td>0.359173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.554217</td>\n",
       "      <td>0.746575</td>\n",
       "      <td>0.271186</td>\n",
       "      <td>0.961240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.439759</td>\n",
       "      <td>0.852740</td>\n",
       "      <td>0.813559</td>\n",
       "      <td>0.612403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.481928</td>\n",
       "      <td>0.496575</td>\n",
       "      <td>0.305085</td>\n",
       "      <td>0.315245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.493976</td>\n",
       "      <td>0.791096</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.860465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Murder   Assault  UrbanPop      Rape\n",
       "0  0.746988  0.654110  0.440678  0.359173\n",
       "1  0.554217  0.746575  0.271186  0.961240\n",
       "2  0.439759  0.852740  0.813559  0.612403\n",
       "3  0.481928  0.496575  0.305085  0.315245\n",
       "4  0.493976  0.791096  1.000000  0.860465"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_norm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create dendrogram\n",
    "dendrogram = sch.dendrogram(sch.linkage(d_norm, method='complete'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create clusters\n",
    "hc = AgglomerativeClustering(n_clusters=4, affinity = 'euclidean', linkage = 'complete')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = hc.fit_predict(d_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 3, 1, 3, 3, 1, 1, 3, 0, 1, 2, 3, 1, 2, 1, 1, 0, 2, 3, 1, 3,\n",
       "       1, 0, 3, 2, 2, 3, 2, 1, 3, 3, 0, 2, 1, 1, 1, 1, 1, 0, 2, 0, 3, 1,\n",
       "       2, 1, 1, 2, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['h_labels'] = labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1=data1.drop(['labels'],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>unnamed</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "      <th>h_labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>13.2</td>\n",
       "      <td>236</td>\n",
       "      <td>58</td>\n",
       "      <td>21.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>10.0</td>\n",
       "      <td>263</td>\n",
       "      <td>48</td>\n",
       "      <td>44.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>8.1</td>\n",
       "      <td>294</td>\n",
       "      <td>80</td>\n",
       "      <td>31.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>8.8</td>\n",
       "      <td>190</td>\n",
       "      <td>50</td>\n",
       "      <td>19.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>9.0</td>\n",
       "      <td>276</td>\n",
       "      <td>91</td>\n",
       "      <td>40.6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>7.9</td>\n",
       "      <td>204</td>\n",
       "      <td>78</td>\n",
       "      <td>38.7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>3.3</td>\n",
       "      <td>110</td>\n",
       "      <td>77</td>\n",
       "      <td>11.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>5.9</td>\n",
       "      <td>238</td>\n",
       "      <td>72</td>\n",
       "      <td>15.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Florida</td>\n",
       "      <td>15.4</td>\n",
       "      <td>335</td>\n",
       "      <td>80</td>\n",
       "      <td>31.9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>17.4</td>\n",
       "      <td>211</td>\n",
       "      <td>60</td>\n",
       "      <td>25.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>5.3</td>\n",
       "      <td>46</td>\n",
       "      <td>83</td>\n",
       "      <td>20.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>2.6</td>\n",
       "      <td>120</td>\n",
       "      <td>54</td>\n",
       "      <td>14.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>10.4</td>\n",
       "      <td>249</td>\n",
       "      <td>83</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>7.2</td>\n",
       "      <td>113</td>\n",
       "      <td>65</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Iowa</td>\n",
       "      <td>2.2</td>\n",
       "      <td>56</td>\n",
       "      <td>57</td>\n",
       "      <td>11.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Kansas</td>\n",
       "      <td>6.0</td>\n",
       "      <td>115</td>\n",
       "      <td>66</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>9.7</td>\n",
       "      <td>109</td>\n",
       "      <td>52</td>\n",
       "      <td>16.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Louisiana</td>\n",
       "      <td>15.4</td>\n",
       "      <td>249</td>\n",
       "      <td>66</td>\n",
       "      <td>22.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Maine</td>\n",
       "      <td>2.1</td>\n",
       "      <td>83</td>\n",
       "      <td>51</td>\n",
       "      <td>7.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Maryland</td>\n",
       "      <td>11.3</td>\n",
       "      <td>300</td>\n",
       "      <td>67</td>\n",
       "      <td>27.8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>4.4</td>\n",
       "      <td>149</td>\n",
       "      <td>85</td>\n",
       "      <td>16.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>12.1</td>\n",
       "      <td>255</td>\n",
       "      <td>74</td>\n",
       "      <td>35.1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>2.7</td>\n",
       "      <td>72</td>\n",
       "      <td>66</td>\n",
       "      <td>14.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Mississippi</td>\n",
       "      <td>16.1</td>\n",
       "      <td>259</td>\n",
       "      <td>44</td>\n",
       "      <td>17.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Missouri</td>\n",
       "      <td>9.0</td>\n",
       "      <td>178</td>\n",
       "      <td>70</td>\n",
       "      <td>28.2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Montana</td>\n",
       "      <td>6.0</td>\n",
       "      <td>109</td>\n",
       "      <td>53</td>\n",
       "      <td>16.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Nebraska</td>\n",
       "      <td>4.3</td>\n",
       "      <td>102</td>\n",
       "      <td>62</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>12.2</td>\n",
       "      <td>252</td>\n",
       "      <td>81</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>2.1</td>\n",
       "      <td>57</td>\n",
       "      <td>56</td>\n",
       "      <td>9.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>New Jersey</td>\n",
       "      <td>7.4</td>\n",
       "      <td>159</td>\n",
       "      <td>89</td>\n",
       "      <td>18.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>11.4</td>\n",
       "      <td>285</td>\n",
       "      <td>70</td>\n",
       "      <td>32.1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>New York</td>\n",
       "      <td>11.1</td>\n",
       "      <td>254</td>\n",
       "      <td>86</td>\n",
       "      <td>26.1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>13.0</td>\n",
       "      <td>337</td>\n",
       "      <td>45</td>\n",
       "      <td>16.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>0.8</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>7.3</td>\n",
       "      <td>120</td>\n",
       "      <td>75</td>\n",
       "      <td>21.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>6.6</td>\n",
       "      <td>151</td>\n",
       "      <td>68</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>4.9</td>\n",
       "      <td>159</td>\n",
       "      <td>67</td>\n",
       "      <td>29.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>6.3</td>\n",
       "      <td>106</td>\n",
       "      <td>72</td>\n",
       "      <td>14.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>3.4</td>\n",
       "      <td>174</td>\n",
       "      <td>87</td>\n",
       "      <td>8.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>South Carolina</td>\n",
       "      <td>14.4</td>\n",
       "      <td>279</td>\n",
       "      <td>48</td>\n",
       "      <td>22.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>South Dakota</td>\n",
       "      <td>3.8</td>\n",
       "      <td>86</td>\n",
       "      <td>45</td>\n",
       "      <td>12.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>13.2</td>\n",
       "      <td>188</td>\n",
       "      <td>59</td>\n",
       "      <td>26.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Texas</td>\n",
       "      <td>12.7</td>\n",
       "      <td>201</td>\n",
       "      <td>80</td>\n",
       "      <td>25.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Utah</td>\n",
       "      <td>3.2</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>22.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>2.2</td>\n",
       "      <td>48</td>\n",
       "      <td>32</td>\n",
       "      <td>11.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>8.5</td>\n",
       "      <td>156</td>\n",
       "      <td>63</td>\n",
       "      <td>20.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Washington</td>\n",
       "      <td>4.0</td>\n",
       "      <td>145</td>\n",
       "      <td>73</td>\n",
       "      <td>26.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>5.7</td>\n",
       "      <td>81</td>\n",
       "      <td>39</td>\n",
       "      <td>9.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>2.6</td>\n",
       "      <td>53</td>\n",
       "      <td>66</td>\n",
       "      <td>10.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>6.8</td>\n",
       "      <td>161</td>\n",
       "      <td>60</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           unnamed  Murder  Assault  UrbanPop  Rape  h_labels\n",
       "0          Alabama    13.2      236        58  21.2         0\n",
       "1           Alaska    10.0      263        48  44.5         0\n",
       "2          Arizona     8.1      294        80  31.0         3\n",
       "3         Arkansas     8.8      190        50  19.5         1\n",
       "4       California     9.0      276        91  40.6         3\n",
       "5         Colorado     7.9      204        78  38.7         3\n",
       "6      Connecticut     3.3      110        77  11.1         1\n",
       "7         Delaware     5.9      238        72  15.8         1\n",
       "8          Florida    15.4      335        80  31.9         3\n",
       "9          Georgia    17.4      211        60  25.8         0\n",
       "10          Hawaii     5.3       46        83  20.2         1\n",
       "11           Idaho     2.6      120        54  14.2         2\n",
       "12        Illinois    10.4      249        83  24.0         3\n",
       "13         Indiana     7.2      113        65  21.0         1\n",
       "14            Iowa     2.2       56        57  11.3         2\n",
       "15          Kansas     6.0      115        66  18.0         1\n",
       "16        Kentucky     9.7      109        52  16.3         1\n",
       "17       Louisiana    15.4      249        66  22.2         0\n",
       "18           Maine     2.1       83        51   7.8         2\n",
       "19        Maryland    11.3      300        67  27.8         3\n",
       "20   Massachusetts     4.4      149        85  16.3         1\n",
       "21        Michigan    12.1      255        74  35.1         3\n",
       "22       Minnesota     2.7       72        66  14.9         1\n",
       "23     Mississippi    16.1      259        44  17.1         0\n",
       "24        Missouri     9.0      178        70  28.2         3\n",
       "25         Montana     6.0      109        53  16.4         2\n",
       "26        Nebraska     4.3      102        62  16.5         2\n",
       "27          Nevada    12.2      252        81  46.0         3\n",
       "28   New Hampshire     2.1       57        56   9.5         2\n",
       "29      New Jersey     7.4      159        89  18.8         1\n",
       "30      New Mexico    11.4      285        70  32.1         3\n",
       "31        New York    11.1      254        86  26.1         3\n",
       "32  North Carolina    13.0      337        45  16.1         0\n",
       "33    North Dakota     0.8       45        44   7.3         2\n",
       "34            Ohio     7.3      120        75  21.4         1\n",
       "35        Oklahoma     6.6      151        68  20.0         1\n",
       "36          Oregon     4.9      159        67  29.3         1\n",
       "37    Pennsylvania     6.3      106        72  14.9         1\n",
       "38    Rhode Island     3.4      174        87   8.3         1\n",
       "39  South Carolina    14.4      279        48  22.5         0\n",
       "40    South Dakota     3.8       86        45  12.8         2\n",
       "41       Tennessee    13.2      188        59  26.9         0\n",
       "42           Texas    12.7      201        80  25.5         3\n",
       "43            Utah     3.2      120        80  22.9         1\n",
       "44         Vermont     2.2       48        32  11.2         2\n",
       "45        Virginia     8.5      156        63  20.7         1\n",
       "46      Washington     4.0      145        73  26.2         1\n",
       "47   West Virginia     5.7       81        39   9.3         2\n",
       "48       Wisconsin     2.6       53        66  10.8         1\n",
       "49         Wyoming     6.8      161        60  15.6         1"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = pd.DataFrame(data1.h_labels.value_counts().rename_axis('clusters').reset_index(name = 'members'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>clusters</th>\n",
       "      <th>members</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   clusters  members\n",
       "0         1       20\n",
       "1         3       12\n",
       "2         2       10\n",
       "3         0        8"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    20\n",
       "3    12\n",
       "2    10\n",
       "0     8\n",
       "Name: h_labels, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.h_labels.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(c.clusters,c.members,color = 'red', width = 0.2)\n",
    "plt.xlabel('cluster')\n",
    "plt.ylabel('no of mem')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>h_labels</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>14.087500</td>\n",
       "      <td>252.750000</td>\n",
       "      <td>53.500000</td>\n",
       "      <td>24.5375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5.715000</td>\n",
       "      <td>132.300000</td>\n",
       "      <td>70.800000</td>\n",
       "      <td>18.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3.180000</td>\n",
       "      <td>78.700000</td>\n",
       "      <td>49.300000</td>\n",
       "      <td>11.6300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>10.883333</td>\n",
       "      <td>256.916667</td>\n",
       "      <td>78.333333</td>\n",
       "      <td>32.2500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  h_labels     Murder     Assault   UrbanPop     Rape\n",
       "                 mean        mean       mean     mean\n",
       "0        0  14.087500  252.750000  53.500000  24.5375\n",
       "1        1   5.715000  132.300000  70.800000  18.1000\n",
       "2        2   3.180000   78.700000  49.300000  11.6300\n",
       "3        3  10.883333  256.916667  78.333333  32.2500"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.groupby('h_labels').agg(['mean']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we can infer from this that cluster 1 have more members and cluster 0 have the least . and assault the crime having the highest average resulting in its frequent occurence\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KMEANS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "nc=[2,3,4,5,6,7,8,9]\n",
    "iner=[]    \n",
    "for i in nc:\n",
    "    km=KMeans(i)\n",
    "    km.fit(d_norm)\n",
    "    iner.append(km.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6.596893867946198,\n",
       " 5.010878493006417,\n",
       " 3.683456153585914,\n",
       " 3.1850219324724334,\n",
       " 2.838486634905374,\n",
       " 2.555478124832634,\n",
       " 2.370623390439918,\n",
       " 2.2129347344552204]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(nc,iner,color=\"black\")\n",
    "plt.scatter(nc,iner,color=\"red\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=4, n_init=10, n_jobs=1, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_kmeans = KMeans(n_clusters=4) \n",
    "model_kmeans.fit(d_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 3, 1, 3, 3, 0, 0, 3, 1, 0, 2, 3, 0, 2, 0, 2, 1, 2, 3, 0, 3,\n",
       "       2, 1, 0, 2, 2, 3, 2, 0, 3, 3, 1, 2, 0, 0, 0, 0, 0, 1, 2, 1, 3, 0,\n",
       "       2, 0, 0, 2, 2, 0])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.683456153585914"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_kmeans.inertia_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "md=pd.Series(model_kmeans.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1[\"k_labels\"]=pd.Series(model_kmeans.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>unnamed</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "      <th>h_labels</th>\n",
       "      <th>k_labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>13.2</td>\n",
       "      <td>236</td>\n",
       "      <td>58</td>\n",
       "      <td>21.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>10.0</td>\n",
       "      <td>263</td>\n",
       "      <td>48</td>\n",
       "      <td>44.5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>8.1</td>\n",
       "      <td>294</td>\n",
       "      <td>80</td>\n",
       "      <td>31.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>8.8</td>\n",
       "      <td>190</td>\n",
       "      <td>50</td>\n",
       "      <td>19.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>9.0</td>\n",
       "      <td>276</td>\n",
       "      <td>91</td>\n",
       "      <td>40.6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>7.9</td>\n",
       "      <td>204</td>\n",
       "      <td>78</td>\n",
       "      <td>38.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>3.3</td>\n",
       "      <td>110</td>\n",
       "      <td>77</td>\n",
       "      <td>11.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>5.9</td>\n",
       "      <td>238</td>\n",
       "      <td>72</td>\n",
       "      <td>15.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Florida</td>\n",
       "      <td>15.4</td>\n",
       "      <td>335</td>\n",
       "      <td>80</td>\n",
       "      <td>31.9</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>17.4</td>\n",
       "      <td>211</td>\n",
       "      <td>60</td>\n",
       "      <td>25.8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>5.3</td>\n",
       "      <td>46</td>\n",
       "      <td>83</td>\n",
       "      <td>20.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>2.6</td>\n",
       "      <td>120</td>\n",
       "      <td>54</td>\n",
       "      <td>14.2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>10.4</td>\n",
       "      <td>249</td>\n",
       "      <td>83</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>7.2</td>\n",
       "      <td>113</td>\n",
       "      <td>65</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Iowa</td>\n",
       "      <td>2.2</td>\n",
       "      <td>56</td>\n",
       "      <td>57</td>\n",
       "      <td>11.3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Kansas</td>\n",
       "      <td>6.0</td>\n",
       "      <td>115</td>\n",
       "      <td>66</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>9.7</td>\n",
       "      <td>109</td>\n",
       "      <td>52</td>\n",
       "      <td>16.3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Louisiana</td>\n",
       "      <td>15.4</td>\n",
       "      <td>249</td>\n",
       "      <td>66</td>\n",
       "      <td>22.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Maine</td>\n",
       "      <td>2.1</td>\n",
       "      <td>83</td>\n",
       "      <td>51</td>\n",
       "      <td>7.8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Maryland</td>\n",
       "      <td>11.3</td>\n",
       "      <td>300</td>\n",
       "      <td>67</td>\n",
       "      <td>27.8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>4.4</td>\n",
       "      <td>149</td>\n",
       "      <td>85</td>\n",
       "      <td>16.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>12.1</td>\n",
       "      <td>255</td>\n",
       "      <td>74</td>\n",
       "      <td>35.1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>2.7</td>\n",
       "      <td>72</td>\n",
       "      <td>66</td>\n",
       "      <td>14.9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Mississippi</td>\n",
       "      <td>16.1</td>\n",
       "      <td>259</td>\n",
       "      <td>44</td>\n",
       "      <td>17.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Missouri</td>\n",
       "      <td>9.0</td>\n",
       "      <td>178</td>\n",
       "      <td>70</td>\n",
       "      <td>28.2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Montana</td>\n",
       "      <td>6.0</td>\n",
       "      <td>109</td>\n",
       "      <td>53</td>\n",
       "      <td>16.4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Nebraska</td>\n",
       "      <td>4.3</td>\n",
       "      <td>102</td>\n",
       "      <td>62</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>12.2</td>\n",
       "      <td>252</td>\n",
       "      <td>81</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>2.1</td>\n",
       "      <td>57</td>\n",
       "      <td>56</td>\n",
       "      <td>9.5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>New Jersey</td>\n",
       "      <td>7.4</td>\n",
       "      <td>159</td>\n",
       "      <td>89</td>\n",
       "      <td>18.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>11.4</td>\n",
       "      <td>285</td>\n",
       "      <td>70</td>\n",
       "      <td>32.1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>New York</td>\n",
       "      <td>11.1</td>\n",
       "      <td>254</td>\n",
       "      <td>86</td>\n",
       "      <td>26.1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>13.0</td>\n",
       "      <td>337</td>\n",
       "      <td>45</td>\n",
       "      <td>16.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>0.8</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>7.3</td>\n",
       "      <td>120</td>\n",
       "      <td>75</td>\n",
       "      <td>21.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>6.6</td>\n",
       "      <td>151</td>\n",
       "      <td>68</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>4.9</td>\n",
       "      <td>159</td>\n",
       "      <td>67</td>\n",
       "      <td>29.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>6.3</td>\n",
       "      <td>106</td>\n",
       "      <td>72</td>\n",
       "      <td>14.9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>3.4</td>\n",
       "      <td>174</td>\n",
       "      <td>87</td>\n",
       "      <td>8.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>South Carolina</td>\n",
       "      <td>14.4</td>\n",
       "      <td>279</td>\n",
       "      <td>48</td>\n",
       "      <td>22.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>South Dakota</td>\n",
       "      <td>3.8</td>\n",
       "      <td>86</td>\n",
       "      <td>45</td>\n",
       "      <td>12.8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>13.2</td>\n",
       "      <td>188</td>\n",
       "      <td>59</td>\n",
       "      <td>26.9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Texas</td>\n",
       "      <td>12.7</td>\n",
       "      <td>201</td>\n",
       "      <td>80</td>\n",
       "      <td>25.5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Utah</td>\n",
       "      <td>3.2</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>22.9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>2.2</td>\n",
       "      <td>48</td>\n",
       "      <td>32</td>\n",
       "      <td>11.2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>8.5</td>\n",
       "      <td>156</td>\n",
       "      <td>63</td>\n",
       "      <td>20.7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Washington</td>\n",
       "      <td>4.0</td>\n",
       "      <td>145</td>\n",
       "      <td>73</td>\n",
       "      <td>26.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>5.7</td>\n",
       "      <td>81</td>\n",
       "      <td>39</td>\n",
       "      <td>9.3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>2.6</td>\n",
       "      <td>53</td>\n",
       "      <td>66</td>\n",
       "      <td>10.8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>6.8</td>\n",
       "      <td>161</td>\n",
       "      <td>60</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           unnamed  Murder  Assault  UrbanPop  Rape  h_labels  k_labels\n",
       "0          Alabama    13.2      236        58  21.2         0         1\n",
       "1           Alaska    10.0      263        48  44.5         0         3\n",
       "2          Arizona     8.1      294        80  31.0         3         3\n",
       "3         Arkansas     8.8      190        50  19.5         1         1\n",
       "4       California     9.0      276        91  40.6         3         3\n",
       "5         Colorado     7.9      204        78  38.7         3         3\n",
       "6      Connecticut     3.3      110        77  11.1         1         0\n",
       "7         Delaware     5.9      238        72  15.8         1         0\n",
       "8          Florida    15.4      335        80  31.9         3         3\n",
       "9          Georgia    17.4      211        60  25.8         0         1\n",
       "10          Hawaii     5.3       46        83  20.2         1         0\n",
       "11           Idaho     2.6      120        54  14.2         2         2\n",
       "12        Illinois    10.4      249        83  24.0         3         3\n",
       "13         Indiana     7.2      113        65  21.0         1         0\n",
       "14            Iowa     2.2       56        57  11.3         2         2\n",
       "15          Kansas     6.0      115        66  18.0         1         0\n",
       "16        Kentucky     9.7      109        52  16.3         1         2\n",
       "17       Louisiana    15.4      249        66  22.2         0         1\n",
       "18           Maine     2.1       83        51   7.8         2         2\n",
       "19        Maryland    11.3      300        67  27.8         3         3\n",
       "20   Massachusetts     4.4      149        85  16.3         1         0\n",
       "21        Michigan    12.1      255        74  35.1         3         3\n",
       "22       Minnesota     2.7       72        66  14.9         1         2\n",
       "23     Mississippi    16.1      259        44  17.1         0         1\n",
       "24        Missouri     9.0      178        70  28.2         3         0\n",
       "25         Montana     6.0      109        53  16.4         2         2\n",
       "26        Nebraska     4.3      102        62  16.5         2         2\n",
       "27          Nevada    12.2      252        81  46.0         3         3\n",
       "28   New Hampshire     2.1       57        56   9.5         2         2\n",
       "29      New Jersey     7.4      159        89  18.8         1         0\n",
       "30      New Mexico    11.4      285        70  32.1         3         3\n",
       "31        New York    11.1      254        86  26.1         3         3\n",
       "32  North Carolina    13.0      337        45  16.1         0         1\n",
       "33    North Dakota     0.8       45        44   7.3         2         2\n",
       "34            Ohio     7.3      120        75  21.4         1         0\n",
       "35        Oklahoma     6.6      151        68  20.0         1         0\n",
       "36          Oregon     4.9      159        67  29.3         1         0\n",
       "37    Pennsylvania     6.3      106        72  14.9         1         0\n",
       "38    Rhode Island     3.4      174        87   8.3         1         0\n",
       "39  South Carolina    14.4      279        48  22.5         0         1\n",
       "40    South Dakota     3.8       86        45  12.8         2         2\n",
       "41       Tennessee    13.2      188        59  26.9         0         1\n",
       "42           Texas    12.7      201        80  25.5         3         3\n",
       "43            Utah     3.2      120        80  22.9         1         0\n",
       "44         Vermont     2.2       48        32  11.2         2         2\n",
       "45        Virginia     8.5      156        63  20.7         1         0\n",
       "46      Washington     4.0      145        73  26.2         1         0\n",
       "47   West Virginia     5.7       81        39   9.3         2         2\n",
       "48       Wisconsin     2.6       53        66  10.8         1         2\n",
       "49         Wyoming     6.8      161        60  15.6         1         0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "c1 = pd.DataFrame(data1.k_labels.value_counts().rename_axis('clusters').reset_index(name = 'members'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>clusters</th>\n",
       "      <th>members</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   clusters  members\n",
       "0         0       17\n",
       "1         2       13\n",
       "2         3       12\n",
       "3         1        8"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(c1.clusters,c1.members,color = 'red', width = 0.2)\n",
    "plt.xlabel('cluster')\n",
    "plt.ylabel('no of mem')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>k_labels</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "      <th>h_labels</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",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5.852941</td>\n",
       "      <td>141.176471</td>\n",
       "      <td>73.647059</td>\n",
       "      <td>19.335294</td>\n",
       "      <td>1.117647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.937500</td>\n",
       "      <td>243.625000</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>21.412500</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>78.538462</td>\n",
       "      <td>52.076923</td>\n",
       "      <td>12.176923</td>\n",
       "      <td>1.769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>10.966667</td>\n",
       "      <td>264.000000</td>\n",
       "      <td>76.500000</td>\n",
       "      <td>33.608333</td>\n",
       "      <td>2.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  k_labels     Murder     Assault   UrbanPop       Rape  h_labels\n",
       "                 mean        mean       mean       mean      mean\n",
       "0        0   5.852941  141.176471  73.647059  19.335294  1.117647\n",
       "1        1  13.937500  243.625000  53.750000  21.412500  0.125000\n",
       "2        2   3.600000   78.538462  52.076923  12.176923  1.769231\n",
       "3        3  10.966667  264.000000  76.500000  33.608333  2.750000"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.groupby('k_labels').agg(['mean']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we can infer from this that cluster 0 have more places and cluster 1 have the least . and assault is the crime having the highest average resulting in its frequent occurence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import DBSCAN \n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#DBSCAN\n",
    "\n",
    "dbs=DBSCAN(min_samples=5,eps=0.3)\n",
    "claas_pr=dbs.fit_predict(d_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0, -1, -1,  1, -1, -1,  1, -1, -1,  0,  1,  1, -1,  1,  1,  1,  1,\n",
       "        0,  1, -1,  1, -1,  1, -1,  1,  1,  1, -1,  1,  1, -1, -1, -1,  1,\n",
       "        1,  1,  1,  1,  1,  0,  1,  0,  1,  1,  1,  1,  1,  1,  1,  1],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "claas_pr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['db_labels'] = claas_pr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>unnamed</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "      <th>h_labels</th>\n",
       "      <th>k_labels</th>\n",
       "      <th>db_labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>13.2</td>\n",
       "      <td>236</td>\n",
       "      <td>58</td>\n",
       "      <td>21.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>10.0</td>\n",
       "      <td>263</td>\n",
       "      <td>48</td>\n",
       "      <td>44.5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>8.1</td>\n",
       "      <td>294</td>\n",
       "      <td>80</td>\n",
       "      <td>31.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>8.8</td>\n",
       "      <td>190</td>\n",
       "      <td>50</td>\n",
       "      <td>19.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>9.0</td>\n",
       "      <td>276</td>\n",
       "      <td>91</td>\n",
       "      <td>40.6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>7.9</td>\n",
       "      <td>204</td>\n",
       "      <td>78</td>\n",
       "      <td>38.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>3.3</td>\n",
       "      <td>110</td>\n",
       "      <td>77</td>\n",
       "      <td>11.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>5.9</td>\n",
       "      <td>238</td>\n",
       "      <td>72</td>\n",
       "      <td>15.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Florida</td>\n",
       "      <td>15.4</td>\n",
       "      <td>335</td>\n",
       "      <td>80</td>\n",
       "      <td>31.9</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>17.4</td>\n",
       "      <td>211</td>\n",
       "      <td>60</td>\n",
       "      <td>25.8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>5.3</td>\n",
       "      <td>46</td>\n",
       "      <td>83</td>\n",
       "      <td>20.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>2.6</td>\n",
       "      <td>120</td>\n",
       "      <td>54</td>\n",
       "      <td>14.2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>10.4</td>\n",
       "      <td>249</td>\n",
       "      <td>83</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>7.2</td>\n",
       "      <td>113</td>\n",
       "      <td>65</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Iowa</td>\n",
       "      <td>2.2</td>\n",
       "      <td>56</td>\n",
       "      <td>57</td>\n",
       "      <td>11.3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Kansas</td>\n",
       "      <td>6.0</td>\n",
       "      <td>115</td>\n",
       "      <td>66</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>9.7</td>\n",
       "      <td>109</td>\n",
       "      <td>52</td>\n",
       "      <td>16.3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Louisiana</td>\n",
       "      <td>15.4</td>\n",
       "      <td>249</td>\n",
       "      <td>66</td>\n",
       "      <td>22.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Maine</td>\n",
       "      <td>2.1</td>\n",
       "      <td>83</td>\n",
       "      <td>51</td>\n",
       "      <td>7.8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Maryland</td>\n",
       "      <td>11.3</td>\n",
       "      <td>300</td>\n",
       "      <td>67</td>\n",
       "      <td>27.8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>4.4</td>\n",
       "      <td>149</td>\n",
       "      <td>85</td>\n",
       "      <td>16.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>12.1</td>\n",
       "      <td>255</td>\n",
       "      <td>74</td>\n",
       "      <td>35.1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>2.7</td>\n",
       "      <td>72</td>\n",
       "      <td>66</td>\n",
       "      <td>14.9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Mississippi</td>\n",
       "      <td>16.1</td>\n",
       "      <td>259</td>\n",
       "      <td>44</td>\n",
       "      <td>17.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Missouri</td>\n",
       "      <td>9.0</td>\n",
       "      <td>178</td>\n",
       "      <td>70</td>\n",
       "      <td>28.2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Montana</td>\n",
       "      <td>6.0</td>\n",
       "      <td>109</td>\n",
       "      <td>53</td>\n",
       "      <td>16.4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Nebraska</td>\n",
       "      <td>4.3</td>\n",
       "      <td>102</td>\n",
       "      <td>62</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>12.2</td>\n",
       "      <td>252</td>\n",
       "      <td>81</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>2.1</td>\n",
       "      <td>57</td>\n",
       "      <td>56</td>\n",
       "      <td>9.5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>New Jersey</td>\n",
       "      <td>7.4</td>\n",
       "      <td>159</td>\n",
       "      <td>89</td>\n",
       "      <td>18.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>11.4</td>\n",
       "      <td>285</td>\n",
       "      <td>70</td>\n",
       "      <td>32.1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>New York</td>\n",
       "      <td>11.1</td>\n",
       "      <td>254</td>\n",
       "      <td>86</td>\n",
       "      <td>26.1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>13.0</td>\n",
       "      <td>337</td>\n",
       "      <td>45</td>\n",
       "      <td>16.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>0.8</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>7.3</td>\n",
       "      <td>120</td>\n",
       "      <td>75</td>\n",
       "      <td>21.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>6.6</td>\n",
       "      <td>151</td>\n",
       "      <td>68</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>4.9</td>\n",
       "      <td>159</td>\n",
       "      <td>67</td>\n",
       "      <td>29.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>6.3</td>\n",
       "      <td>106</td>\n",
       "      <td>72</td>\n",
       "      <td>14.9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>3.4</td>\n",
       "      <td>174</td>\n",
       "      <td>87</td>\n",
       "      <td>8.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>South Carolina</td>\n",
       "      <td>14.4</td>\n",
       "      <td>279</td>\n",
       "      <td>48</td>\n",
       "      <td>22.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>South Dakota</td>\n",
       "      <td>3.8</td>\n",
       "      <td>86</td>\n",
       "      <td>45</td>\n",
       "      <td>12.8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>13.2</td>\n",
       "      <td>188</td>\n",
       "      <td>59</td>\n",
       "      <td>26.9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Texas</td>\n",
       "      <td>12.7</td>\n",
       "      <td>201</td>\n",
       "      <td>80</td>\n",
       "      <td>25.5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Utah</td>\n",
       "      <td>3.2</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>22.9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>2.2</td>\n",
       "      <td>48</td>\n",
       "      <td>32</td>\n",
       "      <td>11.2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>8.5</td>\n",
       "      <td>156</td>\n",
       "      <td>63</td>\n",
       "      <td>20.7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Washington</td>\n",
       "      <td>4.0</td>\n",
       "      <td>145</td>\n",
       "      <td>73</td>\n",
       "      <td>26.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>5.7</td>\n",
       "      <td>81</td>\n",
       "      <td>39</td>\n",
       "      <td>9.3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>2.6</td>\n",
       "      <td>53</td>\n",
       "      <td>66</td>\n",
       "      <td>10.8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>6.8</td>\n",
       "      <td>161</td>\n",
       "      <td>60</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           unnamed  Murder  Assault  UrbanPop  Rape  h_labels  k_labels  \\\n",
       "0          Alabama    13.2      236        58  21.2         0         1   \n",
       "1           Alaska    10.0      263        48  44.5         0         3   \n",
       "2          Arizona     8.1      294        80  31.0         3         3   \n",
       "3         Arkansas     8.8      190        50  19.5         1         1   \n",
       "4       California     9.0      276        91  40.6         3         3   \n",
       "5         Colorado     7.9      204        78  38.7         3         3   \n",
       "6      Connecticut     3.3      110        77  11.1         1         0   \n",
       "7         Delaware     5.9      238        72  15.8         1         0   \n",
       "8          Florida    15.4      335        80  31.9         3         3   \n",
       "9          Georgia    17.4      211        60  25.8         0         1   \n",
       "10          Hawaii     5.3       46        83  20.2         1         0   \n",
       "11           Idaho     2.6      120        54  14.2         2         2   \n",
       "12        Illinois    10.4      249        83  24.0         3         3   \n",
       "13         Indiana     7.2      113        65  21.0         1         0   \n",
       "14            Iowa     2.2       56        57  11.3         2         2   \n",
       "15          Kansas     6.0      115        66  18.0         1         0   \n",
       "16        Kentucky     9.7      109        52  16.3         1         2   \n",
       "17       Louisiana    15.4      249        66  22.2         0         1   \n",
       "18           Maine     2.1       83        51   7.8         2         2   \n",
       "19        Maryland    11.3      300        67  27.8         3         3   \n",
       "20   Massachusetts     4.4      149        85  16.3         1         0   \n",
       "21        Michigan    12.1      255        74  35.1         3         3   \n",
       "22       Minnesota     2.7       72        66  14.9         1         2   \n",
       "23     Mississippi    16.1      259        44  17.1         0         1   \n",
       "24        Missouri     9.0      178        70  28.2         3         0   \n",
       "25         Montana     6.0      109        53  16.4         2         2   \n",
       "26        Nebraska     4.3      102        62  16.5         2         2   \n",
       "27          Nevada    12.2      252        81  46.0         3         3   \n",
       "28   New Hampshire     2.1       57        56   9.5         2         2   \n",
       "29      New Jersey     7.4      159        89  18.8         1         0   \n",
       "30      New Mexico    11.4      285        70  32.1         3         3   \n",
       "31        New York    11.1      254        86  26.1         3         3   \n",
       "32  North Carolina    13.0      337        45  16.1         0         1   \n",
       "33    North Dakota     0.8       45        44   7.3         2         2   \n",
       "34            Ohio     7.3      120        75  21.4         1         0   \n",
       "35        Oklahoma     6.6      151        68  20.0         1         0   \n",
       "36          Oregon     4.9      159        67  29.3         1         0   \n",
       "37    Pennsylvania     6.3      106        72  14.9         1         0   \n",
       "38    Rhode Island     3.4      174        87   8.3         1         0   \n",
       "39  South Carolina    14.4      279        48  22.5         0         1   \n",
       "40    South Dakota     3.8       86        45  12.8         2         2   \n",
       "41       Tennessee    13.2      188        59  26.9         0         1   \n",
       "42           Texas    12.7      201        80  25.5         3         3   \n",
       "43            Utah     3.2      120        80  22.9         1         0   \n",
       "44         Vermont     2.2       48        32  11.2         2         2   \n",
       "45        Virginia     8.5      156        63  20.7         1         0   \n",
       "46      Washington     4.0      145        73  26.2         1         0   \n",
       "47   West Virginia     5.7       81        39   9.3         2         2   \n",
       "48       Wisconsin     2.6       53        66  10.8         1         2   \n",
       "49         Wyoming     6.8      161        60  15.6         1         0   \n",
       "\n",
       "    db_labels  \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         -1  \n",
       "13          1  \n",
       "14          1  \n",
       "15          1  \n",
       "16          1  \n",
       "17          0  \n",
       "18          1  \n",
       "19         -1  \n",
       "20          1  \n",
       "21         -1  \n",
       "22          1  \n",
       "23         -1  \n",
       "24          1  \n",
       "25          1  \n",
       "26          1  \n",
       "27         -1  \n",
       "28          1  \n",
       "29          1  \n",
       "30         -1  \n",
       "31         -1  \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          1  \n",
       "43          1  \n",
       "44          1  \n",
       "45          1  \n",
       "46          1  \n",
       "47          1  \n",
       "48          1  \n",
       "49          1  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "g =pd.DataFrame(data1.db_labels.value_counts().rename_axis('cluster').reset_index(name ='members'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>cluster</th>\n",
       "      <th>members</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cluster  members\n",
       "0        1       31\n",
       "1       -1       14\n",
       "2        0        5"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(g.cluster,g.members,color = 'black', width = 0.2)\n",
    "plt.xlabel('cluster')\n",
    "plt.ylabel('no of mem')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>db_labels</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "      <th>h_labels</th>\n",
       "      <th>k_labels</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",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1</td>\n",
       "      <td>10.992857</td>\n",
       "      <td>271.500000</td>\n",
       "      <td>71.357143</td>\n",
       "      <td>30.485714</td>\n",
       "      <td>2.214286</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>14.720000</td>\n",
       "      <td>232.600000</td>\n",
       "      <td>58.200000</td>\n",
       "      <td>23.720000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>5.222581</td>\n",
       "      <td>115.290323</td>\n",
       "      <td>64.096774</td>\n",
       "      <td>16.651613</td>\n",
       "      <td>1.451613</td>\n",
       "      <td>0.967742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  db_labels     Murder     Assault   UrbanPop       Rape  h_labels  k_labels\n",
       "                  mean        mean       mean       mean      mean      mean\n",
       "0        -1  10.992857  271.500000  71.357143  30.485714  2.214286  2.500000\n",
       "1         0  14.720000  232.600000  58.200000  23.720000  0.000000  1.000000\n",
       "2         1   5.222581  115.290323  64.096774  16.651613  1.451613  0.967742"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.groupby('db_labels').agg(['mean']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we can infer that cluster 1 have more cities and cluster 0 have the least but the urban population avg og cities in cluster -1 is large and the avg rate of crime is also high. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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