risk_tag_clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cba2787e-1ac7-4d47-bafa-106c9cd2c553",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.cluster.hierarchy as sch\n",
    "from scipy.spatial.distance import pdist, squareform\n",
    "from scipy.cluster.hierarchy import fcluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "792a823a-c5ea-49f9-b8c0-58e79b9d2755",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>project_name</th>\n",
       "      <th>Smart contract address</th>\n",
       "      <th>Blog post link</th>\n",
       "      <th>Audit website</th>\n",
       "      <th>Chain</th>\n",
       "      <th>Is_closed_source</th>\n",
       "      <th>hidden_owner</th>\n",
       "      <th>anti_whale_modifiable</th>\n",
       "      <th>Is_anti_whale</th>\n",
       "      <th>Is_honeypot</th>\n",
       "      <th>...</th>\n",
       "      <th>centralized_risk_high</th>\n",
       "      <th>centralized_risk_low</th>\n",
       "      <th>event_setter</th>\n",
       "      <th>external_dependencies</th>\n",
       "      <th>immutable_states</th>\n",
       "      <th>reentrancy_without_eth_transfer</th>\n",
       "      <th>incorrect_inheritance_order</th>\n",
       "      <th>shadowing_local</th>\n",
       "      <th>events_maths</th>\n",
       "      <th>Summary/rationale of risk tags marked true</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Data Analytics</td>\n",
       "      <td>384571416209d08623c6ace9422613fc8970475d</td>\n",
       "      <td>https://chainsecurity.com/security-audit/circl...</td>\n",
       "      <td>https://chainsecurity.com</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Bad Contract: Assigned for flaws that indicate...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Data Analytics</td>\n",
       "      <td>0xAb5801a7D398351b8bE11C439e05C5B3259ae9B</td>\n",
       "      <td>https://stackoverflow.com/questions/75030483/w...</td>\n",
       "      <td>https://studygroup.moralis.io/t/compilation-er...</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "      <td>Summary/rationale of risk tags marked true: Th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Data Analytics</td>\n",
       "      <td>0x4B20993Bc481177ec7E8f571ceCaE8A9e22C02db</td>\n",
       "      <td>https://stackoverflow.com/questions/71115106/s...</td>\n",
       "      <td>https://ethereum.stackexchange.com/questions/1...</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <td>Data Analytics</td>\n",
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       "      <td>https://stackoverflow.com/questions/75030483/w...</td>\n",
       "      <td>https://studygroup.moralis.io/t/compilation-er...</td>\n",
       "      <td>Ethereum</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Data Analytics</td>\n",
       "      <td>0x617F2E2fD72FD9D5503197092aC168c91465E7f2</td>\n",
       "      <td>https://stackoverflow.com/questions/69466137/h...</td>\n",
       "      <td>https://ethereum.stackexchange.com/questions/1...</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "      <td>The smart contract is marked with risks such a...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     project_name                      Smart contract address  \\\n",
       "0  Data Analytics    384571416209d08623c6ace9422613fc8970475d   \n",
       "1  Data Analytics   0xAb5801a7D398351b8bE11C439e05C5B3259ae9B   \n",
       "2  Data Analytics  0x4B20993Bc481177ec7E8f571ceCaE8A9e22C02db   \n",
       "3  Data Analytics  0x78731D3Ca6b7E34aC0F824c42a7cC18A495cabaB   \n",
       "4  Data Analytics  0x617F2E2fD72FD9D5503197092aC168c91465E7f2   \n",
       "\n",
       "                                      Blog post link  \\\n",
       "0  https://chainsecurity.com/security-audit/circl...   \n",
       "1  https://stackoverflow.com/questions/75030483/w...   \n",
       "2  https://stackoverflow.com/questions/71115106/s...   \n",
       "3  https://stackoverflow.com/questions/75030483/w...   \n",
       "4  https://stackoverflow.com/questions/69466137/h...   \n",
       "\n",
       "                                       Audit website     Chain  \\\n",
       "0                          https://chainsecurity.com  Ethereum   \n",
       "1  https://studygroup.moralis.io/t/compilation-er...  Ethereum   \n",
       "2  https://ethereum.stackexchange.com/questions/1...  Ethereum   \n",
       "3  https://studygroup.moralis.io/t/compilation-er...  Ethereum   \n",
       "4  https://ethereum.stackexchange.com/questions/1...  Ethereum   \n",
       "\n",
       "   Is_closed_source  hidden_owner  anti_whale_modifiable  Is_anti_whale  \\\n",
       "0             False         False                  False          False   \n",
       "1             False         False                   True           True   \n",
       "2              True         False                  False           True   \n",
       "3              True         False                  False          False   \n",
       "4              True         False                   True          False   \n",
       "\n",
       "   Is_honeypot  ...  centralized_risk_high  centralized_risk_low  \\\n",
       "0        False  ...                  False                 False   \n",
       "1        False  ...                  False                  True   \n",
       "2        False  ...                  False                  True   \n",
       "3         True  ...                  False                  True   \n",
       "4         True  ...                  False                 False   \n",
       "\n",
       "   event_setter  external_dependencies  immutable_states  \\\n",
       "0          True                   True              True   \n",
       "1         False                   True             False   \n",
       "2         False                   True             False   \n",
       "3         False                  False              True   \n",
       "4          True                   True             False   \n",
       "\n",
       "   reentrancy_without_eth_transfer  incorrect_inheritance_order  \\\n",
       "0                             True                        False   \n",
       "1                            False                         True   \n",
       "2                            False                        False   \n",
       "3                             True                        False   \n",
       "4                             True                        False   \n",
       "\n",
       "   shadowing_local  events_maths  \\\n",
       "0            False         False   \n",
       "1            False          True   \n",
       "2             True          True   \n",
       "3            False          True   \n",
       "4            False          True   \n",
       "\n",
       "          Summary/rationale of risk tags marked true  \n",
       "0  Bad Contract: Assigned for flaws that indicate...  \n",
       "1  Summary/rationale of risk tags marked true: Th...  \n",
       "2                                                NaN  \n",
       "3                                                NaN  \n",
       "4  The smart contract is marked with risks such a...  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_excel('compiled_risk_data.xlsx')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2d6805c6-a07c-4480-889b-d057387589f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['project_name', 'Smart contract address', 'Blog post link',\n",
       "       'Audit website', 'Chain', 'Is_closed_source', 'hidden_owner',\n",
       "       'anti_whale_modifiable', 'Is_anti_whale', 'Is_honeypot', 'buy_tax',\n",
       "       'sell_tax', 'slippage_modifiable', 'Is_blacklisted',\n",
       "       'can_take_back_ownership', 'owner_change_balance', 'is_airdrop_scam',\n",
       "       'selfdestruct', 'trust_list', 'is_whitelisted', 'is_fake_token',\n",
       "       'illegal_unicode', 'exploitation', 'bad_contract',\n",
       "       'reusing_state_variable', 'encode_packed_collision',\n",
       "       'encode_packed_parameters', 'centralized_risk_medium',\n",
       "       'centralized_risk_high', 'centralized_risk_low', 'event_setter',\n",
       "       'external_dependencies', 'immutable_states',\n",
       "       'reentrancy_without_eth_transfer', 'incorrect_inheritance_order',\n",
       "       'shadowing_local', 'events_maths',\n",
       "       'Summary/rationale of risk tags marked true'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0d170cdd-2cd6-4cc0-8b62-cb606b6b81b7",
   "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>exploitation</th>\n",
       "      <th>bad_contract</th>\n",
       "      <th>external_dependencies</th>\n",
       "      <th>centralized_risk_medium</th>\n",
       "      <th>is_airdrop_scam</th>\n",
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      ],
      "text/plain": [
       "   exploitation  bad_contract  external_dependencies  centralized_risk_medium  \\\n",
       "0             1             1                      1                        1   \n",
       "1             0             1                      1                        0   \n",
       "2             1             0                      1                        0   \n",
       "3             1             0                      0                        1   \n",
       "4             1             0                      1                        0   \n",
       "\n",
       "   is_airdrop_scam  \n",
       "0                0  \n",
       "1                0  \n",
       "2                0  \n",
       "3                1  \n",
       "4                0  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new = data.copy()\n",
    "feature_1 = 'exploitation'\n",
    "feature_2 = 'bad_contract'\n",
    "feature_3 = 'external_dependencies'\n",
    "feature_4 = 'centralized_risk_medium'\n",
    "feature_5 = 'is_airdrop_scam'\n",
    "selected_features = data_new[[feature_1, feature_2, feature_3, feature_4, feature_5]].replace({True:1, False:0})\n",
    "selected_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1f2929d3-1f48-4bc6-a74d-98948afb38c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "distance_matrix = pdist(selected_features, 'jaccard')\n",
    "distance_square_matrix = squareform(distance_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d25c030c-e360-43b2-838d-bc4d688cd52f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00000000e+00, 1.10000000e+01, 0.00000000e+00, 2.00000000e+00],\n",
       "       [1.00000000e+00, 9.80000000e+01, 0.00000000e+00, 2.00000000e+00],\n",
       "       [2.00000000e+00, 4.00000000e+00, 0.00000000e+00, 2.00000000e+00],\n",
       "       ...,\n",
       "       [2.17900000e+03, 2.18000000e+03, 7.90947458e+00, 4.72000000e+02],\n",
       "       [2.18100000e+03, 2.18200000e+03, 9.07105523e+00, 6.97000000e+02],\n",
       "       [2.15400000e+03, 2.18300000e+03, 1.95329783e+01, 1.09300000e+03]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linkage_matrix = sch.linkage(distance_matrix, method='ward')\n",
    "linkage_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "cc5e66a9-5e33-4087-9ab8-ab691f1ae1e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "dendrogram = sch.dendrogram(linkage_matrix,\n",
    "                           truncate_mode='lastp',\n",
    "                           p=50)\n",
    "plt.title('Hierarchical Clustering Dendrogram')\n",
    "plt.xlabel('Data points')\n",
    "plt.ylabel('Jaccard distance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "90e8d9d4-8bd5-4beb-b335-ea629f5f7f50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        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",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">exploitation</th>\n",
       "      <th colspan=\"4\" halign=\"left\">bad_contract</th>\n",
       "      <th colspan=\"4\" halign=\"left\">external_dependencies</th>\n",
       "      <th colspan=\"4\" halign=\"left\">centralized_risk_medium</th>\n",
       "      <th colspan=\"4\" halign=\"left\">is_airdrop_scam</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>median</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>median</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>median</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>median</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>median</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>396</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>396</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>396</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>396</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>79</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.678082</td>\n",
       "      <td>0.468820</td>\n",
       "      <td>1.0</td>\n",
       "      <td>146</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>146</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.358569</td>\n",
       "      <td>1.0</td>\n",
       "      <td>120</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.486153</td>\n",
       "      <td>1.0</td>\n",
       "      <td>120</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>120</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>120</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>81</td>\n",
       "      <td>0.407407</td>\n",
       "      <td>0.494413</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>49</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.846847</td>\n",
       "      <td>0.360949</td>\n",
       "      <td>1.0</td>\n",
       "      <td>222</td>\n",
       "      <td>0.684685</td>\n",
       "      <td>0.465691</td>\n",
       "      <td>1.0</td>\n",
       "      <td>222</td>\n",
       "      <td>0.518018</td>\n",
       "      <td>0.500804</td>\n",
       "      <td>1.0</td>\n",
       "      <td>222</td>\n",
       "      <td>0.905405</td>\n",
       "      <td>0.293316</td>\n",
       "      <td>1.0</td>\n",
       "      <td>222</td>\n",
       "      <td>0.310811</td>\n",
       "      <td>0.463871</td>\n",
       "      <td>0.0</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        exploitation                        bad_contract                   \\\n",
       "                mean       std median count         mean       std median   \n",
       "cluster                                                                     \n",
       "1           0.000000  0.000000    0.0   396     0.000000  0.000000    0.0   \n",
       "2           1.000000  0.000000    1.0    79     0.000000  0.000000    0.0   \n",
       "3           0.678082  0.468820    1.0   146     1.000000  0.000000    1.0   \n",
       "4           0.850000  0.358569    1.0   120     0.625000  0.486153    1.0   \n",
       "5           0.000000  0.000000    0.0    81     0.000000  0.000000    0.0   \n",
       "6           0.000000  0.000000    0.0    49     0.000000  0.000000    0.0   \n",
       "7           0.846847  0.360949    1.0   222     0.684685  0.465691    1.0   \n",
       "\n",
       "              external_dependencies                         \\\n",
       "        count                  mean       std median count   \n",
       "cluster                                                      \n",
       "1         396              0.000000  0.000000    0.0   396   \n",
       "2          79              0.000000  0.000000    0.0    79   \n",
       "3         146              0.000000  0.000000    0.0   146   \n",
       "4         120              1.000000  0.000000    1.0   120   \n",
       "5          81              1.000000  0.000000    1.0    81   \n",
       "6          49              0.000000  0.000000    0.0    49   \n",
       "7         222              0.518018  0.500804    1.0   222   \n",
       "\n",
       "        centralized_risk_medium                        is_airdrop_scam  \\\n",
       "                           mean       std median count            mean   \n",
       "cluster                                                                  \n",
       "1                      0.000000  0.000000    0.0   396        0.000000   \n",
       "2                      0.000000  0.000000    0.0    79        0.000000   \n",
       "3                      0.000000  0.000000    0.0   146        0.000000   \n",
       "4                      0.000000  0.000000    0.0   120        0.000000   \n",
       "5                      0.407407  0.494413    0.0    81        0.000000   \n",
       "6                      1.000000  0.000000    1.0    49        0.000000   \n",
       "7                      0.905405  0.293316    1.0   222        0.310811   \n",
       "\n",
       "                                \n",
       "              std median count  \n",
       "cluster                         \n",
       "1        0.000000    0.0   396  \n",
       "2        0.000000    0.0    79  \n",
       "3        0.000000    0.0   146  \n",
       "4        0.000000    0.0   120  \n",
       "5        0.000000    0.0    81  \n",
       "6        0.000000    0.0    49  \n",
       "7        0.463871    0.0   222  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_labels = fcluster(linkage_matrix, t=5, criterion='distance')\n",
    "data_new['cluster'] = cluster_labels\n",
    "cluster_summary = data_new[[feature_1, feature_2, feature_3, feature_4, feature_5,'cluster']].groupby('cluster').agg(['mean', 'std', 'median', 'count'])\n",
    "cluster_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "91a1bff0-11ca-4e98-a507-b2c5734dacab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sVjq41EoD5eXlxtSpU43rrrvO8PT0NG6++Wbj3XffrXUFgZdeesnu/Nqufbn3+HPvvvuu0atXL8PT09Pw9vY2BgwYYPz973+v030u5auvvjImTJhgdOjQwbBarYaXl5fRtWtXIzk52W6Viouf1TAMo7i42Bg3bpwRFBRkeHt7G/Hx8cbRo0ftVhb48ccfjfHjxxtRUVGGr6+v4eXlZXTu3NmYMWOGUVZWZrvWqVOnjF//+tdGixYtDIvFYvz8r6PKykrj5ZdfNnr06GF4enoazZs3N2644Qbj8ccfNw4dOmTrFx4ebtxzzz11eu7y8nLj5ZdfNuLi4oy2bdsaVqvV8PT0NLp06WJMmzbNOHnypF3/nz/Thd/n2rYLf0Yv9H3kkUeM6667znB3dzdat25txMTEGLNmzbL1qevPAoArYzGMOn48FAAaqSNHjuiGG27QjBkzNH36dFeXAwCoR4RbAKbyz3/+U2+//bZiYmLk6+urL7/8UvPmzVNJSYn2799f66oJAABzYM4tAFPx9vbW7t27tWzZMp0+fVp+fn6KjY3Viy++SLAFgF8ARm4BAABgGiwFBgAAANMg3AIAAMA0CLcAAAAwDT5QJun8+fP65ptv5OPjc9Vf5QgAAADnMwxDZ86cUWho6CW/lIdwq5++rz0sLMzVZQAAAOAy8vLy1KZNm1qPE24l+fj4SPrpZfn6+rq4GgAAAFyspKREYWFhttxWG8KtZJuK4OvrS7gFAABowC43hZQPlAEAAMA0CLcAAAAwDcItAAAATINwCwAAANMg3AIAAMA0CLcAAAAwDcItAAAATINwCwAAANMg3AIAAMA0CLcAAAAwjQYTbtPS0mSxWJSUlGRrMwxDqampCg0NlZeXl2JjY3XgwAG788rLyzVp0iQFBATI29tbw4YN04kTJ65x9QAAAGgIGkS4zcnJ0euvv66oqCi79nnz5mnBggVasmSJcnJyFBwcrEGDBunMmTO2PklJScrMzFRGRoZ27typ0tJSDR06VFVVVdf6MQAAAOBiLg+3paWlevDBB/XGG2+oZcuWtnbDMLRo0SI9++yzGj58uCIjI7Vy5Ur98MMPWrNmjSSpuLhYy5Yt0/z58zVw4EDddNNNWr16tfbt26fNmze76pEAAADgIi4Pt4mJibrnnns0cOBAu/YjR46ooKBAgwcPtrVZrVb17dtXu3btkiTt2bNHlZWVdn1CQ0MVGRlp61OT8vJylZSU2G0AAABo/NxcefOMjAzt3btXOTk51Y4VFBRIkoKCguzag4KCdOzYMVsfDw8PuxHfC30unF+TtLQ0zZw582rLBwAAQAPjspHbvLw8PfXUU1q9erU8PT1r7WexWOz2DcOo1naxy/VJSUlRcXGxbcvLy3OseAAAADRILgu3e/bsUWFhoXr27Ck3Nze5ublpx44devXVV+Xm5mYbsb14BLawsNB2LDg4WBUVFSoqKqq1T02sVqt8fX3tNgAAADR+Lgu3AwYM0L59+5Sbm2vboqOj9eCDDyo3N1fXX3+9goODlZWVZTunoqJCO3bsUExMjCSpZ8+ecnd3t+uTn5+v/fv32/oAAADgl8Nlc259fHwUGRlp1+bt7a1WrVrZ2pOSkjR79mx17NhRHTt21OzZs9WsWTM98MADkiQ/Pz+NHTtWU6ZMUatWreTv76+pU6eqe/fu1T6gBgAAAPNz6QfKLmfatGk6e/asJkyYoKKiIvXq1UubNm2Sj4+Prc/ChQvl5uamkSNH6uzZsxowYIBWrFihpk2burDyy4uPd3UFDcP69a6uAAAAmInFMAzD1UW4WklJifz8/FRcXHzN5t8Sbn9CuAUAAHVR17zm8nVuAQAAAGch3AIAAMA0CLcAAAAwDcItAAAATINwCwAAANMg3AIAAMA0CLcAAAAwDcItAAAATINwCwAAANMg3AIAAMA0CLcAAAAwDcItAAAATINwCwAAANMg3AIAAMA0CLcAAAAwDcItAAAATINwCwAAANMg3AIAAMA0CLcAAAAwDcItAAAATINwCwAAANMg3AIAAMA0CLcAAAAwDcItAAAATINwCwAAANMg3AIAAMA0CLcAAAAwDcItAAAATINwCwAAANMg3AIAAMA0CLcAAAAwDcItAAAATINwCwAAANMg3AIAAMA0CLcAAAAwDcItAAAATINwCwAAANMg3AIAAMA0CLcAAAAwDcItAAAATINwCwAAANMg3AIAAMA0CLcAAAAwDcItAAAATINwCwAAANNwabhNT09XVFSUfH195evrqz59+uijjz6yHU9ISJDFYrHbevfubXeN8vJyTZo0SQEBAfL29tawYcN04sSJa/0oAAAAaABcGm7btGmjOXPmaPfu3dq9e7f69++ve++9VwcOHLD1ueuuu5Sfn2/bPvzwQ7trJCUlKTMzUxkZGdq5c6dKS0s1dOhQVVVVXevHAQAAgIu5ufLm8fHxdvsvvvii0tPTlZ2drW7dukmSrFargoODazy/uLhYy5Yt01tvvaWBAwdKklavXq2wsDBt3rxZQ4YMqd8HAAAAQIPSYObcVlVVKSMjQ2VlZerTp4+tffv27QoMDFSnTp306KOPqrCw0HZsz549qqys1ODBg21toaGhioyM1K5du2q9V3l5uUpKSuw2AAAANH4uD7f79u1T8+bNZbVaNX78eGVmZqpr166SpLi4OP31r3/V1q1bNX/+fOXk5Kh///4qLy+XJBUUFMjDw0MtW7a0u2ZQUJAKCgpqvWdaWpr8/PxsW1hYWP09IAAAAK4Zl05LkKTOnTsrNzdXp0+f1jvvvKMxY8Zox44d6tq1q0aNGmXrFxkZqejoaIWHh+uDDz7Q8OHDa72mYRiyWCy1Hk9JSVFycrJtv6SkhIALAABgAi4Ptx4eHurQoYMkKTo6Wjk5OXrllVf05z//uVrfkJAQhYeH69ChQ5Kk4OBgVVRUqKioyG70trCwUDExMbXe02q1ymq1OvlJAAAA4Goun5ZwMcMwbNMOLnby5Enl5eUpJCREktSzZ0+5u7srKyvL1ic/P1/79++/ZLgFAACAObl05Hb69OmKi4tTWFiYzpw5o4yMDG3fvl0bNmxQaWmpUlNTNWLECIWEhOjo0aOaPn26AgICdN9990mS/Pz8NHbsWE2ZMkWtWrWSv7+/pk6dqu7du9tWTwAAAMAvh0vD7bfffqvRo0crPz9ffn5+ioqK0oYNGzRo0CCdPXtW+/bt06pVq3T69GmFhISoX79+Wrt2rXx8fGzXWLhwodzc3DRy5EidPXtWAwYM0IoVK9S0aVMXPhkAAABcwWIYhuHqIlytpKREfn5+Ki4ulq+v7zW550VL/P5irV/v6goAAEBjUNe81uDm3AIAAABXinALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANl4bb9PR0RUVFydfXV76+vurTp48++ugj23HDMJSamqrQ0FB5eXkpNjZWBw4csLtGeXm5Jk2apICAAHl7e2vYsGE6ceLEtX4UAAAANAAuDbdt2rTRnDlztHv3bu3evVv9+/fXvffeawuw8+bN04IFC7RkyRLl5OQoODhYgwYN0pkzZ2zXSEpKUmZmpjIyMrRz506VlpZq6NChqqqqctVjAQAAwEUshmEYri7i5/z9/fXSSy/pkUceUWhoqJKSkvT0009L+mmUNigoSHPnztXjjz+u4uJitW7dWm+99ZZGjRolSfrmm28UFhamDz/8UEOGDKnTPUtKSuTn56fi4mL5+vrW27P9XHz8NblNg7d+vasrAAAAjUFd81qDmXNbVVWljIwMlZWVqU+fPjpy5IgKCgo0ePBgWx+r1aq+fftq165dkqQ9e/aosrLSrk9oaKgiIyNtfWpSXl6ukpISuw0AAACNn8vD7b59+9S8eXNZrVaNHz9emZmZ6tq1qwoKCiRJQUFBdv2DgoJsxwoKCuTh4aGWLVvW2qcmaWlp8vPzs21hYWFOfioAAAC4gsvDbefOnZWbm6vs7Gw98cQTGjNmjL744gvbcYvFYtffMIxqbRe7XJ+UlBQVFxfbtry8vKt7CAAAADQILg+3Hh4e6tChg6Kjo5WWlqYePXrolVdeUXBwsCRVG4EtLCy0jeYGBweroqJCRUVFtfapidVqta3QcGEDAABA4+fycHsxwzBUXl6uiIgIBQcHKysry3asoqJCO3bsUExMjCSpZ8+ecnd3t+uTn5+v/fv32/oAAADgl8PNlTefPn264uLiFBYWpjNnzigjI0Pbt2/Xhg0bZLFYlJSUpNmzZ6tjx47q2LGjZs+erWbNmumBBx6QJPn5+Wns2LGaMmWKWrVqJX9/f02dOlXdu3fXwIEDXfloAAAAcAGXhttvv/1Wo0ePVn5+vvz8/BQVFaUNGzZo0KBBkqRp06bp7NmzmjBhgoqKitSrVy9t2rRJPj4+tmssXLhQbm5uGjlypM6ePasBAwZoxYoVatq0qaseCwAAAC7S4Na5dQXWuXUd1rkFAAB10ejWuQUAAACuFuEWAAAApkG4BQAAgGkQbgEAAGAahFsAAACYBuEWAAAApkG4BQAAgGkQbgEAAGAahFsAAACYBuEWAAAApkG4BQAAgGkQbgEAAGAahFsAAACYBuEWAAAApkG4BQAAgGkQbgEAAGAahFsAAACYBuEWAAAApkG4BQAAgGkQbgEAAGAahFsAAACYBuEWAAAApkG4BQAAgGkQbgEAAGAahFsAAACYBuEWAAAApkG4BQAAgGkQbgEAAGAahFsAAACYBuEWAAAApkG4BQAAgGkQbgEAAGAahFsAAACYBuEWAAAApkG4BQAAgGkQbgEAAGAahFsAAACYhlPC7enTp51xGQAAAOCqOBxu586dq7Vr19r2R44cqVatWum6667TP//5T6cWBwAAADjC4XD75z//WWFhYZKkrKwsZWVl6aOPPlJcXJx+//vfO71AAAAAoK7cHD0hPz/fFm7ff/99jRw5UoMHD1a7du3Uq1cvpxcIAAAA1JXDI7ctW7ZUXl6eJGnDhg0aOHCgJMkwDFVVVTm3OgAAAMABDo/cDh8+XA888IA6duyokydPKi4uTpKUm5urDh06OL1AAAAAoK4cDrcLFy5Uu3btlJeXp3nz5ql58+aSfpquMGHCBKcXCAAAANSVw+H2H//4h5KSkuTmZn/qxIkTtWvXLqcVBgAAADjK4Tm3/fr106lTp6q1FxcXq1+/fg5dKy0tTbfccot8fHwUGBioX/3qV/ryyy/t+iQkJMhisdhtvXv3tutTXl6uSZMmKSAgQN7e3ho2bJhOnDjh6KMBAACgkXM43BqGIYvFUq395MmT8vb2duhaO3bsUGJiorKzs5WVlaVz585p8ODBKisrs+t31113KT8/37Z9+OGHdseTkpKUmZmpjIwM7dy5U6WlpRo6dCgfcAMAAPiFqfO0hOHDh0uSLBaLEhISZLVabceqqqr0+eefKyYmxqGbb9iwwW5/+fLlCgwM1J49e3TnnXfa2q1Wq4KDg2u8RnFxsZYtW6a33nrLtnLD6tWrFRYWps2bN2vIkCEO1QQAAIDGq87h1s/PT9JPI7c+Pj7y8vKyHfPw8FDv3r316KOPXlUxxcXFkiR/f3+79u3btyswMFAtWrRQ37599eKLLyowMFCStGfPHlVWVmrw4MG2/qGhoYqMjNSuXbtqDLfl5eUqLy+37ZeUlFxV3QAAAGgY6hxuly9fLklq166dpk6d6vAUhMsxDEPJycm6/fbbFRkZaWuPi4vTb37zG4WHh+vIkSN67rnn1L9/f+3Zs0dWq1UFBQXy8PBQy5Yt7a4XFBSkgoKCGu+VlpammTNnOrV+AAAAuJ7DqyXMmDGjPurQxIkT9fnnn2vnzp127aNGjbL9OjIyUtHR0QoPD9cHH3xgmypRk9rmBktSSkqKkpOTbfslJSW2b10DAABA4+XwB8q+/fZbjR49WqGhoXJzc1PTpk3ttisxadIkvffee9q2bZvatGlzyb4hISEKDw/XoUOHJEnBwcGqqKhQUVGRXb/CwkIFBQXVeA2r1SpfX1+7DQAAAI2fwyO3CQkJOn78uJ577jmFhITUOjpaF4ZhaNKkScrMzNT27dsVERFx2XNOnjypvLw8hYSESJJ69uwpd3d3ZWVlaeTIkZJ++kKJ/fv3a968eVdcGwAAABofh8Ptzp079cknn+jGG2+86psnJiZqzZo1+tvf/iYfHx/bHFk/Pz95eXmptLRUqampGjFihEJCQnT06FFNnz5dAQEBuu+++2x9x44dqylTpqhVq1by9/fX1KlT1b17d9vqCQAAAPhlcDjchoWFyTAMp9w8PT1dkhQbG2vXvnz5ciUkJKhp06bat2+fVq1apdOnTyskJET9+vXT2rVr5ePjY+u/cOFCubm5aeTIkTp79qwGDBigFStWXPE0CQAAADROFsPBpLpp0ybNnz9ff/7zn9WuXbt6KuvaKikpkZ+fn4qLi6/Z/Nv4+GtymwZv/XpXVwAAABqDuuY1h0duR40apR9++EHt27dXs2bN5O7ubne8pq/mBQAAAK4Fh8PtokWL6qEMAAAA4Oo5HG7HjBlTH3UAAAAAV61O4bakpMQ2t+FyX1XLmrEAAABwlTqF25YtWyo/P1+BgYFq0aJFjWvbXvhGsKqqKqcXCQAAANRFncLt1q1b5e/vL0natm1bvRYEAAAAXKk6hdu+ffvW+GsAAACgIXH4A2WSdPr0aS1btkwHDx6UxWJR165d9cgjj8jPz8/Z9QEAAAB11sTRE3bv3q327dtr4cKFOnXqlL7//nstWLBA7du31969e+ujRgAAAKBOHB65nTx5soYNG6Y33nhDbm4/nX7u3DmNGzdOSUlJ+vjjj51eJAAAAFAXDofb3bt32wVbSXJzc9O0adMUHR3t1OIAAAAARzg8LcHX11fHjx+v1p6XlycfHx+nFAUAAABcCYfD7ahRozR27FitXbtWeXl5OnHihDIyMjRu3Dj99re/rY8aAQAAgDpxeFrCyy+/LIvFoocffljnzp2TJLm7u+uJJ57QnDlznF4gAAAAUFcWwzCMKznxhx9+0FdffSXDMNShQwc1a9bM2bVdMyUlJfLz81NxcfE1+/rg+PhrcpsGb/16V1cAAAAag7rmtTpPS/jhhx+UmJio6667ToGBgRo3bpxCQkIUFRXVqIMtAAAAzKPO4XbGjBlasWKF7rnnHt1///3KysrSE088UZ+1AQAAAA6p85zbdevWadmyZbr//vslSQ899JBuu+02VVVVqWnTpvVWIAAAAFBXdR65zcvL0x133GHbv/XWW+Xm5qZvvvmmXgoDAAAAHFXncFtVVSUPDw+7Njc3N9uKCQAAAICr1XlagmEYSkhIkNVqtbX9+OOPGj9+vLy9vW1t69atc26FAAAAQB3VOdyOGTOmWttDDz3k1GIAAACAq1HncLt8+fL6rAMAAAC4ag5//S4AAADQUDn89bsAAAC/ZHzL6H81xG8aZeQWAAAApkG4BQAAgGnUKdzefPPNKioqkiS98MIL+uGHH+q1KAAAAOBK1CncHjx4UGVlZZKkmTNnqrS0tF6LAgAAAK5EnT5QduONN+p3v/udbr/9dhmGoZdfflnNmzevse/zzz/v1AIBAACAuqpTuF2xYoVmzJih999/XxaLRR999JHc3KqfarFYCLcAAABwmTqF286dOysjI0OS1KRJE23ZskWBgYH1WhgAAADgKIfXuT1//nx91AEAAABctSv6EoevvvpKixYt0sGDB2WxWNSlSxc99dRTat++vbPrAwAAAOrM4XVuN27cqK5du+rTTz9VVFSUIiMj9X//93/q1q2bsrKy6qNGAAAAoE4cHrl95plnNHnyZM2ZM6da+9NPP61BgwY5rTgAAADAEQ6P3B48eFBjx46t1v7II4/oiy++cEpRAAAAwJVwONy2bt1aubm51dpzc3NZQQEAAAAu5fC0hEcffVSPPfaYvv76a8XExMhisWjnzp2aO3eupkyZUh81AgAAAHXicLh97rnn5OPjo/nz5yslJUWSFBoaqtTUVD355JNOLxAAAACoK4fDrcVi0eTJkzV58mSdOXNGkuTj4+P0wgAAAABHXdE6txcQagEAANCQOPyBMgAAAKChItwCAADANAi3AAAAMA2Hwm1lZaX69eunf//73065eVpamm655Rb5+PgoMDBQv/rVr/Tll1/a9TEMQ6mpqQoNDZWXl5diY2N14MABuz7l5eWaNGmSAgIC5O3trWHDhunEiRNOqREAAACNh0Ph1t3dXfv375fFYnHKzXfs2KHExERlZ2crKytL586d0+DBg1VWVmbrM2/ePC1YsEBLlixRTk6OgoODNWjQINtKDZKUlJSkzMxMZWRkaOfOnSotLdXQoUNVVVXllDoBAADQOFgMwzAcOWHKlClyd3fXnDlznF7Md999p8DAQO3YsUN33nmnDMNQaGiokpKS9PTTT0v6aZQ2KChIc+fO1eOPP67i4mK1bt1ab731lkaNGiVJ+uabbxQWFqYPP/xQQ4YMqXaf8vJylZeX2/ZLSkoUFham4uJi+fr6Ov25ahIff01u0+CtX+/qCgAAcAx/h//Xtfx7vKSkRH5+fpfNaw4vBVZRUaG//OUvysrKUnR0tLy9ve2OL1iwwPFq/7/i4mJJkr+/vyTpyJEjKigo0ODBg219rFar+vbtq127dunxxx/Xnj17VFlZadcnNDRUkZGR2rVrV43hNi0tTTNnzrziOgEAANAwORxu9+/fr5tvvlmSqs29vZrpCoZhKDk5WbfffrsiIyMlSQUFBZKkoKAgu75BQUE6duyYrY+Hh4datmxZrc+F8y+WkpKi5ORk2/6FkVsAAAA0bg6H223bttVHHZo4caI+//xz7dy5s9qxi0OzYRiXDdKX6mO1WmW1Wq+8WAAAADRIV7wU2OHDh7Vx40adPXtW0k9h8kpNmjRJ7733nrZt26Y2bdrY2oODgyWp2ghsYWGhbTQ3ODhYFRUVKioqqrUPAAAAfhkcDrcnT57UgAED1KlTJ919993Kz8+XJI0bN05Tpkxx6FqGYWjixIlat26dtm7dqoiICLvjERERCg4OVlZWlq2toqJCO3bsUExMjCSpZ8+ecnd3t+uTn5+v/fv32/oAAADgl8HhcDt58mS5u7vr+PHjatasma191KhR2rBhg0PXSkxM1OrVq7VmzRr5+PiooKBABQUFttFgi8WipKQkzZ49W5mZmdq/f78SEhLUrFkzPfDAA5IkPz8/jR07VlOmTNGWLVv02Wef6aGHHlL37t01cOBARx8PAAAAjZjDc243bdqkjRs32k0fkKSOHTvaPuRVV+np6ZKk2NhYu/bly5crISFBkjRt2jSdPXtWEyZMUFFRkXr16qVNmzbJx8fH1n/hwoVyc3PTyJEjdfbsWQ0YMEArVqxQ06ZNHX08AAAANGIOh9uysjK7EdsLvv/+e4c/pFWXeboWi0WpqalKTU2ttY+np6cWL16sxYsXO3R/AAAAmIvD0xLuvPNOrVq1yrZvsVh0/vx5vfTSS+rXr59TiwMAAAAc4fDI7UsvvaTY2Fjt3r1bFRUVmjZtmg4cOKBTp07p73//e33UCAAAANSJwyO3Xbt21eeff65bb71VgwYNUllZmYYPH67PPvtM7du3r48aAQAAgDpxeORW+mltWb6+FgAAAA3NFYXboqIiLVu2TAcPHpTFYlGXLl30u9/9Tv7+/s6uDwAAAKgzh6cl7NixQxEREXr11VdVVFSkU6dO6dVXX1VERIR27NhRHzUCAAAAdeLwyG1iYqJGjhyp9PR02zqyVVVVmjBhghITE7V//36nFwkAAADUhcMjt1999ZWmTJli9wUJTZs2VXJysr766iunFgcAAAA4wuFwe/PNN+vgwYPV2g8ePKgbb7zRGTUBAAAAV6RO0xI+//xz26+ffPJJPfXUUzp8+LB69+4tScrOztZrr72mOXPm1E+VAAAAQB1YjDp8B26TJk1ksVgu+3W5FotFVVVVTivuWikpKZGfn5+Ki4vl6+t7Te4ZH39NbtPgrV/v6goAAHAMf4f/17X8e7yuea1OI7dHjhxxWmEAAABAfalTuA0PD6/vOgAAAICrdkVf4vCf//xHf//731VYWKjz58/bHXvyySedUhgAAADgKIfD7fLlyzV+/Hh5eHioVatWslgstmMWi4VwCwAAAJdxONw+//zzev7555WSkqImTRxeSQwAAACoNw6n0x9++EH3338/wRYAAAANjsMJdezYsfqf//mf+qgFAAAAuCoOT0tIS0vT0KFDtWHDBnXv3l3u7u52xxcsWOC04gAAAABHOBxuZ8+erY0bN6pz586SVO0DZQAAAICrOBxuFyxYoDfffFMJCQn1UA4AAABw5Ryec2u1WnXbbbfVRy0AAADAVXE43D711FNavHhxfdQCAAAAXBWHpyV8+umn2rp1q95//31169at2gfK1q1b57TiAAAAAEc4HG5btGih4cOH10ctAAAAwFW5oq/fBQAAABoivmYMAAAApuHwyG1ERMQl17P9+uuvr6ogAAAA4Eo5HG6TkpLs9isrK/XZZ59pw4YN+v3vf++sugAAAACHORxun3rqqRrbX3vtNe3evfuqCwIAAACulNPm3MbFxemdd95x1uUAAAAAhzkt3P7v//6v/P39nXU5AAAAwGEOT0u46aab7D5QZhiGCgoK9N1332np0qVOLQ4AAABwhMPh9le/+pXdfpMmTdS6dWvFxsbqhhtucFZdAAAAgMMcDrczZsyojzoAAACAq8aXOAAAAMA06jxy26RJk0t+eYMkWSwWnTt37qqLAgAAAK5EncNtZmZmrcd27dqlxYsXyzAMpxQFAAAAXIk6h9t77723Wtu//vUvpaSkaP369XrwwQf1xz/+0anFAQAAAI64ojm333zzjR599FFFRUXp3Llzys3N1cqVK9W2bVtn1wcAAADUmUPhtri4WE8//bQ6dOigAwcOaMuWLVq/fr0iIyPrqz4AAACgzuo8LWHevHmaO3eugoOD9fbbb9c4TQEAAABwpTqH22eeeUZeXl7q0KGDVq5cqZUrV9bYb926dU4rDgAAAHBEncPtww8/fNmlwAAAAABXqnO4XbFihdNv/vHHH+ull17Snj17lJ+fr8zMTLuv901ISKg2QtyrVy9lZ2fb9svLyzV16lS9/fbbOnv2rAYMGKClS5eqTZs2Tq8XAAAADZtLv6GsrKxMPXr00JIlS2rtc9dddyk/P9+2ffjhh3bHk5KSlJmZqYyMDO3cuVOlpaUaOnSoqqqq6rt8AAAANDB1HrmtD3FxcYqLi7tkH6vVquDg4BqPFRcXa9myZXrrrbc0cOBASdLq1asVFhamzZs3a8iQIU6vGQAAAA2XS0du62L79u0KDAxUp06d9Oijj6qwsNB2bM+ePaqsrNTgwYNtbaGhoYqMjNSuXbtqvWZ5eblKSkrsNgAAADR+DTrcxsXF6a9//au2bt2q+fPnKycnR/3791d5ebkkqaCgQB4eHmrZsqXdeUFBQSooKKj1umlpafLz87NtYWFh9focAAAAuDZcOi3hckaNGmX7dWRkpKKjoxUeHq4PPvhAw4cPr/U8wzAuubJDSkqKkpOTbfslJSUEXAAAABNo0CO3FwsJCVF4eLgOHTokSQoODlZFRYWKiors+hUWFiooKKjW61itVvn6+tptAAAAaPwaVbg9efKk8vLyFBISIknq2bOn3N3dlZWVZeuTn5+v/fv3KyYmxlVlAgAAwEVcOi2htLRUhw8ftu0fOXJEubm58vf3l7+/v1JTUzVixAiFhITo6NGjmj59ugICAnTfffdJkvz8/DR27FhNmTJFrVq1kr+/v6ZOnaru3bvbVk8AAADAL4dLw+3u3bvVr18/2/6FebBjxoxRenq69u3bp1WrVun06dMKCQlRv379tHbtWvn4+NjOWbhwodzc3DRy5EjblzisWLFCTZs2vebPAzR28fGurqBhWL/e1RUAAK6US8NtbGysDMOo9fjGjRsvew1PT08tXrxYixcvdmZpAAAAaIQa1ZxbAAAA4FIItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANN1cXAABmFB/v6goahvXrXV0BgF8aRm4BAABgGoRbAAAAmAbhFgAAAKZBuAUAAIBpEG4BAABgGoRbAAAAmAbhFgAAAKZBuAUAAIBpEG4BAABgGoRbAAAAmAbhFgAAAKZBuAUAAIBpEG4BAABgGoRbAAAAmAbhFgAAAKZBuAUAAIBpEG4BAABgGoRbAAAAmIZLw+3HH3+s+Ph4hYaGymKx6N1337U7bhiGUlNTFRoaKi8vL8XGxurAgQN2fcrLyzVp0iQFBATI29tbw4YN04kTJ67hUwAAAKChcGm4LSsrU48ePbRkyZIaj8+bN08LFizQkiVLlJOTo+DgYA0aNEhnzpyx9UlKSlJmZqYyMjK0c+dOlZaWaujQoaqqqrpWjwEAAIAGws2VN4+Li1NcXFyNxwzD0KJFi/Tss89q+PDhkqSVK1cqKChIa9as0eOPP67i4mItW7ZMb731lgYOHChJWr16tcLCwrR582YNGTLkmj0LAAAAXK/Bzrk9cuSICgoKNHjwYFub1WpV3759tWvXLknSnj17VFlZadcnNDRUkZGRtj41KS8vV0lJid0GAACAxq/BhtuCggJJUlBQkF17UFCQ7VhBQYE8PDzUsmXLWvvUJC0tTX5+frYtLCzMydUDAADAFRpsuL3AYrHY7RuGUa3tYpfrk5KSouLiYtuWl5fnlFoBAADgWg023AYHB0tStRHYwsJC22hucHCwKioqVFRUVGufmlitVvn6+tptAAAAaPwabLiNiIhQcHCwsrKybG0VFRXasWOHYmJiJEk9e/aUu7u7XZ/8/Hzt37/f1gcAAAC/HC5dLaG0tFSHDx+27R85ckS5ubny9/dX27ZtlZSUpNmzZ6tjx47q2LGjZs+erWbNmumBBx6QJPn5+Wns2LGaMmWKWrVqJX9/f02dOlXdu3e3rZ4AAACAXw6Xhtvdu3erX79+tv3k5GRJ0pgxY7RixQpNmzZNZ8+e1YQJE1RUVKRevXpp06ZN8vHxsZ2zcOFCubm5aeTIkTp79qwGDBigFStWqGnTptf8eQAAAOBaFsMwDFcX4WolJSXy8/NTcXHxNZt/Gx9/TW7T4K1f7+oK8HP8ufyJM/5c8i5/ws84zIif7/+6lj/jdc1rDXbOLQAAAOAowi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMw83VBQAAcCnx8a6uoGFYv97VFQCNAyO3AAAAMA1GbtHoMarzE0Z1AABg5BYAAAAmQrgFAACAaRBuAQAAYBqEWwAAAJgG4RYAAACmQbgFAACAaRBuAQAAYBqEWwAAAJgG4RYAAACmQbgFAACAaRBuAQAAYBqEWwAAAJgG4RYAAACmQbgFAACAaRBuAQAAYBqEWwAAAJgG4RYAAACmQbgFAACAaRBuAQAAYBqEWwAAAJgG4RYAAACmQbgFAACAaRBuAQAAYBqEWwAAAJgG4RYAAACmQbgFAACAaRBuAQAAYBoNOtympqbKYrHYbcHBwbbjhmEoNTVVoaGh8vLyUmxsrA4cOODCigEAAOBKDTrcSlK3bt2Un59v2/bt22c7Nm/ePC1YsEBLlixRTk6OgoODNWjQIJ05c8aFFQMAAMBVGny4dXNzU3BwsG1r3bq1pJ9GbRctWqRnn31Ww4cPV2RkpFauXKkffvhBa9ascXHVAAAAcIUGH24PHTqk0NBQRURE6P7779fXX38tSTpy5IgKCgo0ePBgW1+r1aq+fftq165dl7xmeXm5SkpK7DYAAAA0fg063Pbq1UurVq3Sxo0b9cYbb6igoEAxMTE6efKkCgoKJElBQUF25wQFBdmO1SYtLU1+fn62LSwsrN6eAQAAANeOm6sLuJS4uDjbr7t3764+ffqoffv2WrlypXr37i1JslgsducYhlGt7WIpKSlKTk627ZeUlBBwAQCmFx/v6goahvXrXV0B6lODHrm9mLe3t7p3765Dhw7ZVk24eJS2sLCw2mjuxaxWq3x9fe02AAAANH6NKtyWl5fr4MGDCgkJUUREhIKDg5WVlWU7XlFRoR07digmJsaFVQIAAMBVGvS0hKlTpyo+Pl5t27ZVYWGhZs2apZKSEo0ZM0YWi0VJSUmaPXu2OnbsqI4dO2r27Nlq1qyZHnjgAVeXDgAAABdo0OH2xIkT+u1vf6vvv/9erVu3Vu/evZWdna3w8HBJ0rRp03T27FlNmDBBRUVF6tWrlzZt2iQfHx8XVw4AAABXaNDhNiMj45LHLRaLUlNTlZqaem0KAgAAQIPWqObcAgAAAJdCuAUAAIBpEG4BAABgGoRbAAAAmAbhFgAAAKZBuAUAAIBpEG4BAABgGoRbAAAAmAbhFgAAAKZBuAUAAIBpEG4BAABgGoRbAAAAmAbhFgAAAKZBuAUAAIBpEG4BAABgGoRbAAAAmAbhFgAAAKZBuAUAAIBpEG4BAABgGoRbAAAAmAbhFgAAAKZBuAUAAIBpEG4BAABgGoRbAAAAmAbhFgAAAKZBuAUAAIBpEG4BAABgGoRbAAAAmAbhFgAAAKZBuAUAAIBpEG4BAABgGoRbAAAAmAbhFgAAAKZBuAUAAIBpEG4BAABgGoRbAAAAmAbhFgAAAKZBuAUAAIBpEG4BAABgGoRbAAAAmAbhFgAAAKZBuAUAAIBpEG4BAABgGoRbAAAAmAbhFgAAAKZhmnC7dOlSRUREyNPTUz179tQnn3zi6pIAAABwjZki3K5du1ZJSUl69tln9dlnn+mOO+5QXFycjh8/7urSAAAAcA2ZItwuWLBAY8eO1bhx49SlSxctWrRIYWFhSk9Pd3VpAAAAuIbcXF3A1aqoqNCePXv0zDPP2LUPHjxYu3btqvGc8vJylZeX2/aLi4slSSUlJfVX6EUqK6/ZrRo0Z7xy3uVPeJfOw7t0Ht6l8/Aunedq3yXv8b+uYXSy5TTDMC7Zr9GH2++//15VVVUKCgqyaw8KClJBQUGN56SlpWnmzJnV2sPCwuqlRtTOz8/VFZgH79J5eJfOw7t0Ht6l8/AunccV7/LMmTPyu8SNG324vcBisdjtG4ZRre2ClJQUJScn2/bPnz+vU6dOqVWrVrWeYzYlJSUKCwtTXl6efH19XV1Oo8a7dB7epfPwLp2Hd+k8vEvn+SW+S8MwdObMGYWGhl6yX6MPtwEBAWratGm1UdrCwsJqo7kXWK1WWa1Wu7YWLVrUV4kNmq+v7y/mh6K+8S6dh3fpPLxL5+FdOg/v0nl+ae/yUiO2FzT6D5R5eHioZ8+eysrKsmvPyspSTEyMi6oCAACAKzT6kVtJSk5O1ujRoxUdHa0+ffro9ddf1/HjxzV+/HhXlwYAAIBryBThdtSoUTp58qReeOEF5efnKzIyUh9++KHCw8NdXVqDZbVaNWPGjGrTM+A43qXz8C6dh3fpPLxL5+FdOg/vsnYW43LrKQAAAACNRKOfcwsAAABcQLgFAACAaRBuAQAAYBqEWwAAAJgG4fYX5uOPP1Z8fLxCQ0NlsVj07rvvurqkRistLU233HKLfHx8FBgYqF/96lf68ssvXV1Wo5Senq6oqCjbYuR9+vTRRx995OqyGr20tDRZLBYlJSW5upRGJzU1VRaLxW4LDg52dVmN1n/+8x899NBDatWqlZo1a6Ybb7xRe/bscXVZjU67du2q/bm0WCxKTEx0dWkNCuH2F6asrEw9evTQkiVLXF1Ko7djxw4lJiYqOztbWVlZOnfunAYPHqyysjJXl9botGnTRnPmzNHu3bu1e/du9e/fX/fee68OHDjg6tIarZycHL3++uuKiopydSmNVrdu3ZSfn2/b9u3b5+qSGqWioiLddtttcnd310cffaQvvvhC8+fP/8V+M+jVyMnJsfszeeELrH7zm9+4uLKGxRTr3KLu4uLiFBcX5+oyTGHDhg12+8uXL1dgYKD27NmjO++800VVNU7x8fF2+y+++KLS09OVnZ2tbt26uaiqxqu0tFQPPvig3njjDc2aNcvV5TRabm5ujNY6wdy5cxUWFqbly5fb2tq1a+e6ghqx1q1b2+3PmTNH7du3V9++fV1UUcPEyC3gJMXFxZIkf39/F1fSuFVVVSkjI0NlZWXq06ePq8tplBITE3XPPfdo4MCBri6lUTt06JBCQ0MVERGh+++/X19//bWrS2qU3nvvPUVHR+s3v/mNAgMDddNNN+mNN95wdVmNXkVFhVavXq1HHnlEFovF1eU0KIRbwAkMw1BycrJuv/12RUZGurqcRmnfvn1q3ry5rFarxo8fr8zMTHXt2tXVZTU6GRkZ2rt3r9LS0lxdSqPWq1cvrVq1Shs3btQbb7yhgoICxcTE6OTJk64urdH5+uuvlZ6ero4dO2rjxo0aP368nnzySa1atcrVpTVq7777rk6fPq2EhARXl9LgMC0BcIKJEyfq888/186dO11dSqPVuXNn5ebm6vTp03rnnXc0ZswY7dixg4DrgLy8PD311FPatGmTPD09XV1Oo/bz6Vvdu3dXnz591L59e61cuVLJyckurKzxOX/+vKKjozV79mxJ0k033aQDBw4oPT1dDz/8sIura7yWLVumuLg4hYaGurqUBoeRW+AqTZo0Se+99562bdumNm3auLqcRsvDw0MdOnRQdHS00tLS1KNHD73yyiuuLqtR2bNnjwoLC9WzZ0+5ubnJzc1NO3bs0Kuvvio3NzdVVVW5usRGy9vbW927d9ehQ4dcXUqjExISUu0/Urt06aLjx4+7qKLG79ixY9q8ebPGjRvn6lIaJEZugStkGIYmTZqkzMxMbd++XREREa4uyVQMw1B5ebmry2hUBgwYUO0T/b/73e90ww036Omnn1bTpk1dVFnjV15eroMHD+qOO+5wdSmNzm233VZtmcR///vfCg8Pd1FFjd+FDzDfc889ri6lQSLc/sKUlpbq8OHDtv0jR44oNzdX/v7+atu2rQsra3wSExO1Zs0a/e1vf5OPj48KCgokSX5+fvLy8nJxdY3L9OnTFRcXp7CwMJ05c0YZGRnavn17tRUpcGk+Pj7V5nx7e3urVatWzAV30NSpUxUfH6+2bduqsLBQs2bNUklJicaMGePq0hqdyZMnKyYmRrNnz9bIkSP16aef6vXXX9frr7/u6tIapfPnz2v58uUaM2aM3NyIcTXhrfzC7N69W/369bPtX5g7NmbMGK1YscJFVTVO6enpkqTY2Fi79uXLlzPB30HffvutRo8erfz8fPn5+SkqKkobNmzQoEGDXF0afqFOnDih3/72t/r+++/VunVr9e7dW9nZ2Yw2XoFbbrlFmZmZSklJ0QsvvKCIiAgtWrRIDz74oKtLa5Q2b96s48eP65FHHnF1KQ2WxTAMw9VFAAAAAM7AB8oAAABgGoRbAAAAmAbhFgAAAKZBuAUAAIBpEG4BAABgGoRbAAAAmAbhFgAAAKZBuAUAAIBpEG4BoIGzWCx69913XV0GADQKhFsAcLGCggJNmjRJ119/vaxWq8LCwhQfH68tW7Y4/V7bt2+XxWLR6dOnnX5tAGgI3FxdAAD8kh09elS33XabWrRooXnz5ikqKkqVlZXauHGjEhMT9a9//cvVJdbIMAxVVVXJzY2/RgA0LIzcAoALTZgwQRaLRZ9++ql+/etfq1OnTurWrZuSk5OVnZ1drX9NI6+5ubmyWCw6evSoJOnYsWOKj49Xy5Yt5e3trW7duunDDz/U0aNH1a9fP0lSy5YtZbFYlJCQIOmnsDpv3jxdf/318vLyUo8ePfS///u/1e67ceNGRUdHy2q16pNPPqm39wIAV4r/5AYAFzl16pQ2bNigF198Ud7e3tWOt2jR4oqum5iYqIqKCn388cfy9vbWF198oebNmyssLEzvvPOORowYoS+//FK+vr7y8vKSJP3hD3/QunXrlJ6ero4dO+rjjz/WQw89pNatW6tv3762a0+bNk0vv/yyrr/++iuuDwDqE+EWAFzk8OHDMgxDN9xwg1Ove/z4cY0YMULdu3eXJF1//fW2Y/7+/pKkwMBAWzgtKyvTggULtHXrVvXp08d2zs6dO/XnP//ZLty+8MILGjRokFPrBQBnItwCgIsYhiHpp9UQnOnJJ5/UE088oU2bNmngwIEaMWKEoqKiau3/xRdf6Mcff6wWWisqKnTTTTfZtUVHRzu1VgBwNubcAoCLdOzYURaLRQcPHqzzOU2a/PR/2xeCsSRVVlba9Rk3bpy+/vprjR49Wvv27VN0dLQWL15c6zXPnz8vSfrggw+Um5tr27744gu7ebeSapw+AQANCeEWAFzE399fQ4YM0WuvvaaysrJqx2tarqt169aSpPz8fFtbbm5utX5hYWEaP3681q1bpylTpuiNN96QJHl4eEiSqqqqbH27du0qq9Wq48ePq0OHDnZbWFjY1TwiAFxzhFsAcKGlS5eqqqpKt956q9555x0dOnRIBw8e1Kuvvmqb//pzFwJnamqq/v3vf+uDDz7Q/Pnz7fokJSVp48aNOnLkiPbu3autW7eqS5cukqTw8HBZLBa9//77+u6771RaWiofHx9NnTpVkydP1sqVK/XVV1/ps88+02uvvaaVK1dek/cAAM5CuAUAF4qIiNDevXvVr18/TZkyRZGRkRo0aJC2bNmi9PT0av3d3d319ttv61//+pd69OihuXPnatasWXZ9qqqqlJiYqC5duuiuu+5S586dtXTpUknSddddp5kzZ+qZZ55RUFCQJk6cKEn64x//qOeff15paWnq0qWLhgwZovXr1ysiIqL+XwIAOJHF+PnELQAAAKARY+QWAAAApkG4BQAAgGkQbgEAAGAahFsAAACYBuEWAAAApkG4BQAAgGkQbgEAAGAahFsAAACYBuEWAAAApkG4BQAAgGkQbgEAAGAa/w90CXC3t0eyxQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.hist(cluster_labels, bins=np.arange(1, np.max(cluster_labels)+2)-0.5, rwidth=0.8, color='blue', alpha=0.7)\n",
    "plt.title('Histogram of Cluster Sizes')\n",
    "plt.xlabel('Cluster')\n",
    "plt.ylabel('Number of Points')\n",
    "plt.xticks(np.arange(1, np.max(cluster_labels)+1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "fcfb8f84-f7ef-49f1-9960-732703193c1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_centers = data_new[[feature_1, feature_2, feature_3, feature_4, feature_5,'cluster']].groupby('cluster').mean()\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.heatmap(cluster_centers, annot=True, cmap='coolwarm')\n",
    "plt.title('Heatmap of Cluster Centroids')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}