cryptocurrency_clusters.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies and Set up\n",
    "import requests\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fetching API\n",
    "# url = 'https://min-api.cryptocompare.com/data/all/coinlist'\n",
    "# r = requests.get(url)\n",
    "# data = r.json()\n",
    "# data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting Data from dictionary, reading and tanspose DF\n",
    "# data = data['Data']\n",
    "# data = pd.DataFrame.from_dict(data)\n",
    "# data = data.T\n",
    "# data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>CoinName</th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>IsTrading</th>\n",
       "      <th>ProofType</th>\n",
       "      <th>TotalCoinsMined</th>\n",
       "      <th>TotalCoinSupply</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42</td>\n",
       "      <td>42 Coin</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>True</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>4.199995e+01</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>365</td>\n",
       "      <td>365Coin</td>\n",
       "      <td>X11</td>\n",
       "      <td>True</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2300000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>404</td>\n",
       "      <td>404Coin</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>True</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>1.055185e+09</td>\n",
       "      <td>532000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>611</td>\n",
       "      <td>SixEleven</td>\n",
       "      <td>SHA-256</td>\n",
       "      <td>True</td>\n",
       "      <td>PoW</td>\n",
       "      <td>NaN</td>\n",
       "      <td>611000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>808</td>\n",
       "      <td>808</td>\n",
       "      <td>SHA-256</td>\n",
       "      <td>True</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247</th>\n",
       "      <td>XBC</td>\n",
       "      <td>BitcoinPlus</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>True</td>\n",
       "      <td>PoS</td>\n",
       "      <td>1.283270e+05</td>\n",
       "      <td>1000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1248</th>\n",
       "      <td>DVTC</td>\n",
       "      <td>DivotyCoin</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>False</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>2.149121e+07</td>\n",
       "      <td>100000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1249</th>\n",
       "      <td>GIOT</td>\n",
       "      <td>Giotto Coin</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>False</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>233100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>OPSC</td>\n",
       "      <td>OpenSourceCoin</td>\n",
       "      <td>SHA-256</td>\n",
       "      <td>False</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1251</th>\n",
       "      <td>PUNK</td>\n",
       "      <td>SteamPunk</td>\n",
       "      <td>PoS</td>\n",
       "      <td>False</td>\n",
       "      <td>PoS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1252 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0        CoinName Algorithm  IsTrading ProofType  \\\n",
       "0            42         42 Coin    Scrypt       True   PoW/PoS   \n",
       "1           365         365Coin       X11       True   PoW/PoS   \n",
       "2           404         404Coin    Scrypt       True   PoW/PoS   \n",
       "3           611       SixEleven   SHA-256       True       PoW   \n",
       "4           808             808   SHA-256       True   PoW/PoS   \n",
       "...         ...             ...       ...        ...       ...   \n",
       "1247        XBC     BitcoinPlus    Scrypt       True       PoS   \n",
       "1248       DVTC      DivotyCoin    Scrypt      False   PoW/PoS   \n",
       "1249       GIOT     Giotto Coin    Scrypt      False   PoW/PoS   \n",
       "1250       OPSC  OpenSourceCoin   SHA-256      False   PoW/PoS   \n",
       "1251       PUNK       SteamPunk       PoS      False       PoS   \n",
       "\n",
       "      TotalCoinsMined TotalCoinSupply  \n",
       "0        4.199995e+01              42  \n",
       "1                 NaN      2300000000  \n",
       "2        1.055185e+09       532000000  \n",
       "3                 NaN          611000  \n",
       "4        0.000000e+00               0  \n",
       "...               ...             ...  \n",
       "1247     1.283270e+05         1000000  \n",
       "1248     2.149121e+07       100000000  \n",
       "1249              NaN       233100000  \n",
       "1250              NaN        21000000  \n",
       "1251              NaN        40000000  \n",
       "\n",
       "[1252 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Opening csv\n",
    "data = pd.read_csv('Resource/crypto_data.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Columns\n",
    "# data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping columns\n",
    "# data.drop(columns = [\n",
    "#     'Url',\n",
    "#     'ImageUrl',\n",
    "#     'ContentCreatedOn',\n",
    "#     'Name',\n",
    "#     'Symbol',\n",
    "#     'FullName',\n",
    "#     'Description',\n",
    "#     'AssetTokenStatus',\n",
    "#     'SortOrder',\n",
    "#     'Sponsored',\n",
    "#     'Taxonomy',\n",
    "#     'Rating',\n",
    "#     'CirculatingSupply',\n",
    "#     'BlockNumber',\n",
    "#     'NetHashesPerSecond',\n",
    "#     'BlockReward',\n",
    "#     'BlockTime',\n",
    "#     'AssetLaunchDate',\n",
    "#     'AssetWhitepaperUrl',\n",
    "#     'MaxSupply',\n",
    "#     'MktCapPenalty',\n",
    "#     'IsUsedInDefi',\n",
    "#     'IsUsedInNft',\n",
    "#     'PlatformType',\n",
    "#     'BuiltOn',\n",
    "#     'SmartContractAddress',\n",
    "#     'DecimalPoints',\n",
    "#     'Difficulty',\n",
    "#     'AlgorithmType',\n",
    "#     'AssetWebsiteUrl'\n",
    "# ],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>CoinName</th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>IsTrading</th>\n",
       "      <th>ProofType</th>\n",
       "      <th>TotalCoinsMined</th>\n",
       "      <th>TotalCoinSupply</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42</td>\n",
       "      <td>42 Coin</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>True</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>4.199995e+01</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>365</td>\n",
       "      <td>365Coin</td>\n",
       "      <td>X11</td>\n",
       "      <td>True</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2300000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>404</td>\n",
       "      <td>404Coin</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>True</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>1.055185e+09</td>\n",
       "      <td>532000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>611</td>\n",
       "      <td>SixEleven</td>\n",
       "      <td>SHA-256</td>\n",
       "      <td>True</td>\n",
       "      <td>PoW</td>\n",
       "      <td>NaN</td>\n",
       "      <td>611000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>808</td>\n",
       "      <td>808</td>\n",
       "      <td>SHA-256</td>\n",
       "      <td>True</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1243</th>\n",
       "      <td>SERO</td>\n",
       "      <td>Super Zero</td>\n",
       "      <td>Ethash</td>\n",
       "      <td>True</td>\n",
       "      <td>PoW</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1244</th>\n",
       "      <td>UOS</td>\n",
       "      <td>UOS</td>\n",
       "      <td>SHA-256</td>\n",
       "      <td>True</td>\n",
       "      <td>DPoI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1245</th>\n",
       "      <td>BDX</td>\n",
       "      <td>Beldex</td>\n",
       "      <td>CryptoNight</td>\n",
       "      <td>True</td>\n",
       "      <td>PoW</td>\n",
       "      <td>9.802226e+08</td>\n",
       "      <td>1400222610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1246</th>\n",
       "      <td>ZEN</td>\n",
       "      <td>Horizen</td>\n",
       "      <td>Equihash</td>\n",
       "      <td>True</td>\n",
       "      <td>PoW</td>\n",
       "      <td>7.296538e+06</td>\n",
       "      <td>21000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247</th>\n",
       "      <td>XBC</td>\n",
       "      <td>BitcoinPlus</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>True</td>\n",
       "      <td>PoS</td>\n",
       "      <td>1.283270e+05</td>\n",
       "      <td>1000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1144 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0     CoinName    Algorithm  IsTrading ProofType  \\\n",
       "0            42      42 Coin       Scrypt       True   PoW/PoS   \n",
       "1           365      365Coin          X11       True   PoW/PoS   \n",
       "2           404      404Coin       Scrypt       True   PoW/PoS   \n",
       "3           611    SixEleven      SHA-256       True       PoW   \n",
       "4           808          808      SHA-256       True   PoW/PoS   \n",
       "...         ...          ...          ...        ...       ...   \n",
       "1243       SERO   Super Zero       Ethash       True       PoW   \n",
       "1244        UOS          UOS      SHA-256       True      DPoI   \n",
       "1245        BDX       Beldex  CryptoNight       True       PoW   \n",
       "1246        ZEN      Horizen     Equihash       True       PoW   \n",
       "1247        XBC  BitcoinPlus       Scrypt       True       PoS   \n",
       "\n",
       "      TotalCoinsMined TotalCoinSupply  \n",
       "0        4.199995e+01              42  \n",
       "1                 NaN      2300000000  \n",
       "2        1.055185e+09       532000000  \n",
       "3                 NaN          611000  \n",
       "4        0.000000e+00               0  \n",
       "...               ...             ...  \n",
       "1243              NaN      1000000000  \n",
       "1244              NaN      1000000000  \n",
       "1245     9.802226e+08      1400222610  \n",
       "1246     7.296538e+06        21000000  \n",
       "1247     1.283270e+05         1000000  \n",
       "\n",
       "[1144 rows x 7 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Discard all cryptocurrencies that are not being traded\n",
    "cdata = data.loc[data['IsTrading']== True]\n",
    "cdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the IsTrading column from the dataframe\n",
    "cdata = cdata.drop(columns = 'IsTrading')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0           0\n",
       "CoinName             0\n",
       "Algorithm            0\n",
       "ProofType            0\n",
       "TotalCoinsMined    459\n",
       "TotalCoinSupply      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove all rows that have at least one null value\n",
    "cdata.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>CoinName</th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>ProofType</th>\n",
       "      <th>TotalCoinsMined</th>\n",
       "      <th>TotalCoinSupply</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42</td>\n",
       "      <td>42 Coin</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>4.199995e+01</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>404</td>\n",
       "      <td>404Coin</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>1.055185e+09</td>\n",
       "      <td>532000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>808</td>\n",
       "      <td>808</td>\n",
       "      <td>SHA-256</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1337</td>\n",
       "      <td>EliteCoin</td>\n",
       "      <td>X13</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>2.927942e+10</td>\n",
       "      <td>314159265359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>SHA-256</td>\n",
       "      <td>PoW</td>\n",
       "      <td>1.792718e+07</td>\n",
       "      <td>21000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1238</th>\n",
       "      <td>ZEPH</td>\n",
       "      <td>ZEPHYR</td>\n",
       "      <td>SHA-256</td>\n",
       "      <td>DPoS</td>\n",
       "      <td>2.000000e+09</td>\n",
       "      <td>2000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1242</th>\n",
       "      <td>GAP</td>\n",
       "      <td>Gapcoin</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>1.493105e+07</td>\n",
       "      <td>250000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1245</th>\n",
       "      <td>BDX</td>\n",
       "      <td>Beldex</td>\n",
       "      <td>CryptoNight</td>\n",
       "      <td>PoW</td>\n",
       "      <td>9.802226e+08</td>\n",
       "      <td>1400222610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1246</th>\n",
       "      <td>ZEN</td>\n",
       "      <td>Horizen</td>\n",
       "      <td>Equihash</td>\n",
       "      <td>PoW</td>\n",
       "      <td>7.296538e+06</td>\n",
       "      <td>21000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247</th>\n",
       "      <td>XBC</td>\n",
       "      <td>BitcoinPlus</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>PoS</td>\n",
       "      <td>1.283270e+05</td>\n",
       "      <td>1000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>685 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0     CoinName    Algorithm ProofType  TotalCoinsMined  \\\n",
       "0            42      42 Coin       Scrypt   PoW/PoS     4.199995e+01   \n",
       "2           404      404Coin       Scrypt   PoW/PoS     1.055185e+09   \n",
       "4           808          808      SHA-256   PoW/PoS     0.000000e+00   \n",
       "5          1337    EliteCoin          X13   PoW/PoS     2.927942e+10   \n",
       "7           BTC      Bitcoin      SHA-256       PoW     1.792718e+07   \n",
       "...         ...          ...          ...       ...              ...   \n",
       "1238       ZEPH       ZEPHYR      SHA-256      DPoS     2.000000e+09   \n",
       "1242        GAP      Gapcoin       Scrypt   PoW/PoS     1.493105e+07   \n",
       "1245        BDX       Beldex  CryptoNight       PoW     9.802226e+08   \n",
       "1246        ZEN      Horizen     Equihash       PoW     7.296538e+06   \n",
       "1247        XBC  BitcoinPlus       Scrypt       PoS     1.283270e+05   \n",
       "\n",
       "     TotalCoinSupply  \n",
       "0                 42  \n",
       "2          532000000  \n",
       "4                  0  \n",
       "5       314159265359  \n",
       "7           21000000  \n",
       "...              ...  \n",
       "1238      2000000000  \n",
       "1242       250000000  \n",
       "1245      1400222610  \n",
       "1246        21000000  \n",
       "1247         1000000  \n",
       "\n",
       "[685 rows x 6 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping nulls\n",
    "cdata = cdata.dropna()\n",
    "cdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>CoinName</th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>ProofType</th>\n",
       "      <th>TotalCoinsMined</th>\n",
       "      <th>TotalCoinSupply</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42</td>\n",
       "      <td>42 Coin</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>4.199995e+01</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>404</td>\n",
       "      <td>404Coin</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>1.055185e+09</td>\n",
       "      <td>532000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1337</td>\n",
       "      <td>EliteCoin</td>\n",
       "      <td>X13</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>2.927942e+10</td>\n",
       "      <td>314159265359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>SHA-256</td>\n",
       "      <td>PoW</td>\n",
       "      <td>1.792718e+07</td>\n",
       "      <td>21000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ETH</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>Ethash</td>\n",
       "      <td>PoW</td>\n",
       "      <td>1.076842e+08</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1238</th>\n",
       "      <td>ZEPH</td>\n",
       "      <td>ZEPHYR</td>\n",
       "      <td>SHA-256</td>\n",
       "      <td>DPoS</td>\n",
       "      <td>2.000000e+09</td>\n",
       "      <td>2000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1242</th>\n",
       "      <td>GAP</td>\n",
       "      <td>Gapcoin</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>1.493105e+07</td>\n",
       "      <td>250000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1245</th>\n",
       "      <td>BDX</td>\n",
       "      <td>Beldex</td>\n",
       "      <td>CryptoNight</td>\n",
       "      <td>PoW</td>\n",
       "      <td>9.802226e+08</td>\n",
       "      <td>1400222610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1246</th>\n",
       "      <td>ZEN</td>\n",
       "      <td>Horizen</td>\n",
       "      <td>Equihash</td>\n",
       "      <td>PoW</td>\n",
       "      <td>7.296538e+06</td>\n",
       "      <td>21000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247</th>\n",
       "      <td>XBC</td>\n",
       "      <td>BitcoinPlus</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>PoS</td>\n",
       "      <td>1.283270e+05</td>\n",
       "      <td>1000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>532 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0     CoinName    Algorithm ProofType  TotalCoinsMined  \\\n",
       "0            42      42 Coin       Scrypt   PoW/PoS     4.199995e+01   \n",
       "2           404      404Coin       Scrypt   PoW/PoS     1.055185e+09   \n",
       "5          1337    EliteCoin          X13   PoW/PoS     2.927942e+10   \n",
       "7           BTC      Bitcoin      SHA-256       PoW     1.792718e+07   \n",
       "8           ETH     Ethereum       Ethash       PoW     1.076842e+08   \n",
       "...         ...          ...          ...       ...              ...   \n",
       "1238       ZEPH       ZEPHYR      SHA-256      DPoS     2.000000e+09   \n",
       "1242        GAP      Gapcoin       Scrypt   PoW/PoS     1.493105e+07   \n",
       "1245        BDX       Beldex  CryptoNight       PoW     9.802226e+08   \n",
       "1246        ZEN      Horizen     Equihash       PoW     7.296538e+06   \n",
       "1247        XBC  BitcoinPlus       Scrypt       PoS     1.283270e+05   \n",
       "\n",
       "     TotalCoinSupply  \n",
       "0                 42  \n",
       "2          532000000  \n",
       "5       314159265359  \n",
       "7           21000000  \n",
       "8                  0  \n",
       "...              ...  \n",
       "1238      2000000000  \n",
       "1242       250000000  \n",
       "1245      1400222610  \n",
       "1246        21000000  \n",
       "1247         1000000  \n",
       "\n",
       "[532 rows x 6 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter for cryptocurrencies that have been mined.\n",
    "cdata = cdata.loc[cdata['TotalCoinsMined'] > 0]\n",
    "cdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0         0\n",
       "CoinName           0\n",
       "Algorithm          0\n",
       "ProofType          0\n",
       "TotalCoinsMined    0\n",
       "TotalCoinSupply    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Veryfiying nulls\n",
    "cdata.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           42 Coin\n",
       "2           404Coin\n",
       "5         EliteCoin\n",
       "7           Bitcoin\n",
       "8          Ethereum\n",
       "           ...     \n",
       "1238         ZEPHYR\n",
       "1242        Gapcoin\n",
       "1245         Beldex\n",
       "1246        Horizen\n",
       "1247    BitcoinPlus\n",
       "Name: CoinName, Length: 532, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Saving CoinName (just in case)\n",
    "labels= cdata['CoinName']\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/karlaflores/opt/anaconda3/lib/python3.8/site-packages/pandas/core/frame.py:4163: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().drop(\n"
     ]
    },
    {
     "data": {
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BTC</td>\n",
       "      <td>SHA-256</td>\n",
       "      <td>PoW</td>\n",
       "      <td>1.792718e+07</td>\n",
       "      <td>21000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ETH</td>\n",
       "      <td>Ethash</td>\n",
       "      <td>PoW</td>\n",
       "      <td>1.076842e+08</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1238</th>\n",
       "      <td>ZEPH</td>\n",
       "      <td>SHA-256</td>\n",
       "      <td>DPoS</td>\n",
       "      <td>2.000000e+09</td>\n",
       "      <td>2000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1242</th>\n",
       "      <td>GAP</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>1.493105e+07</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1245</th>\n",
       "      <td>BDX</td>\n",
       "      <td>CryptoNight</td>\n",
       "      <td>PoW</td>\n",
       "      <td>9.802226e+08</td>\n",
       "      <td>1400222610</td>\n",
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       "    <tr>\n",
       "      <th>1246</th>\n",
       "      <td>ZEN</td>\n",
       "      <td>Equihash</td>\n",
       "      <td>PoW</td>\n",
       "      <td>7.296538e+06</td>\n",
       "      <td>21000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247</th>\n",
       "      <td>XBC</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>PoS</td>\n",
       "      <td>1.283270e+05</td>\n",
       "      <td>1000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>532 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0    Algorithm ProofType  TotalCoinsMined TotalCoinSupply\n",
       "0            42       Scrypt   PoW/PoS     4.199995e+01              42\n",
       "2           404       Scrypt   PoW/PoS     1.055185e+09       532000000\n",
       "5          1337          X13   PoW/PoS     2.927942e+10    314159265359\n",
       "7           BTC      SHA-256       PoW     1.792718e+07        21000000\n",
       "8           ETH       Ethash       PoW     1.076842e+08               0\n",
       "...         ...          ...       ...              ...             ...\n",
       "1238       ZEPH      SHA-256      DPoS     2.000000e+09      2000000000\n",
       "1242        GAP       Scrypt   PoW/PoS     1.493105e+07       250000000\n",
       "1245        BDX  CryptoNight       PoW     9.802226e+08      1400222610\n",
       "1246        ZEN     Equihash       PoW     7.296538e+06        21000000\n",
       "1247        XBC       Scrypt       PoS     1.283270e+05         1000000\n",
       "\n",
       "[532 rows x 5 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping column\n",
    "cdata.drop(columns ='CoinName', inplace = True)\n",
    "cdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting up index\n",
    "cdata = cdata.set_index('Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(532, 98)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating dummy variables\n",
    "X = pd.get_dummies(cdata, columns = ['ProofType','Algorithm'])\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>2.927942e+10</td>\n",
       "      <td>314159265359</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BTC</th>\n",
       "      <td>1.792718e+07</td>\n",
       "      <td>21000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ETH</th>\n",
       "      <td>1.076842e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZEPH</th>\n",
       "      <td>2.000000e+09</td>\n",
       "      <td>2000000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GAP</th>\n",
       "      <td>1.493105e+07</td>\n",
       "      <td>250000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BDX</th>\n",
       "      <td>9.802226e+08</td>\n",
       "      <td>1400222610</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZEN</th>\n",
       "      <td>7.296538e+06</td>\n",
       "      <td>21000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XBC</th>\n",
       "      <td>1.283270e+05</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>532 rows × 98 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            TotalCoinsMined TotalCoinSupply  ProofType_DPOS  ProofType_DPoS  \\\n",
       "Unnamed: 0                                                                    \n",
       "42             4.199995e+01              42               0               0   \n",
       "404            1.055185e+09       532000000               0               0   \n",
       "1337           2.927942e+10    314159265359               0               0   \n",
       "BTC            1.792718e+07        21000000               0               0   \n",
       "ETH            1.076842e+08               0               0               0   \n",
       "...                     ...             ...             ...             ...   \n",
       "ZEPH           2.000000e+09      2000000000               0               1   \n",
       "GAP            1.493105e+07       250000000               0               0   \n",
       "BDX            9.802226e+08      1400222610               0               0   \n",
       "ZEN            7.296538e+06        21000000               0               0   \n",
       "XBC            1.283270e+05         1000000               0               0   \n",
       "\n",
       "            ProofType_HPoW  ProofType_LPoS  ProofType_POBh  ProofType_PoA  \\\n",
       "Unnamed: 0                                                                  \n",
       "42                       0               0               0              0   \n",
       "404                      0               0               0              0   \n",
       "1337                     0               0               0              0   \n",
       "BTC                      0               0               0              0   \n",
       "ETH                      0               0               0              0   \n",
       "...                    ...             ...             ...            ...   \n",
       "ZEPH                     0               0               0              0   \n",
       "GAP                      0               0               0              0   \n",
       "BDX                      0               0               0              0   \n",
       "ZEN                      0               0               0              0   \n",
       "XBC                      0               0               0              0   \n",
       "\n",
       "            ProofType_PoC  ProofType_PoS  ...  Algorithm_Tribus  \\\n",
       "Unnamed: 0                                ...                     \n",
       "42                      0              0  ...                 0   \n",
       "404                     0              0  ...                 0   \n",
       "1337                    0              0  ...                 0   \n",
       "BTC                     0              0  ...                 0   \n",
       "ETH                     0              0  ...                 0   \n",
       "...                   ...            ...  ...               ...   \n",
       "ZEPH                    0              0  ...                 0   \n",
       "GAP                     0              0  ...                 0   \n",
       "BDX                     0              0  ...                 0   \n",
       "ZEN                     0              0  ...                 0   \n",
       "XBC                     0              1  ...                 0   \n",
       "\n",
       "            Algorithm_VBFT  Algorithm_VeChainThor Authority  Algorithm_X11  \\\n",
       "Unnamed: 0                                                                   \n",
       "42                       0                                0              0   \n",
       "404                      0                                0              0   \n",
       "1337                     0                                0              0   \n",
       "BTC                      0                                0              0   \n",
       "ETH                      0                                0              0   \n",
       "...                    ...                              ...            ...   \n",
       "ZEPH                     0                                0              0   \n",
       "GAP                      0                                0              0   \n",
       "BDX                      0                                0              0   \n",
       "ZEN                      0                                0              0   \n",
       "XBC                      0                                0              0   \n",
       "\n",
       "            Algorithm_X11GOST  Algorithm_X13  Algorithm_X14  Algorithm_X15  \\\n",
       "Unnamed: 0                                                                   \n",
       "42                          0              0              0              0   \n",
       "404                         0              0              0              0   \n",
       "1337                        0              1              0              0   \n",
       "BTC                         0              0              0              0   \n",
       "ETH                         0              0              0              0   \n",
       "...                       ...            ...            ...            ...   \n",
       "ZEPH                        0              0              0              0   \n",
       "GAP                         0              0              0              0   \n",
       "BDX                         0              0              0              0   \n",
       "ZEN                         0              0              0              0   \n",
       "XBC                         0              0              0              0   \n",
       "\n",
       "            Algorithm_X16R  Algorithm_XEVAN  \n",
       "Unnamed: 0                                   \n",
       "42                       0                0  \n",
       "404                      0                0  \n",
       "1337                     0                0  \n",
       "BTC                      0                0  \n",
       "ETH                      0                0  \n",
       "...                    ...              ...  \n",
       "ZEPH                     0                0  \n",
       "GAP                      0                0  \n",
       "BDX                      0                0  \n",
       "ZEN                      0                0  \n",
       "XBC                      0                0  \n",
       "\n",
       "[532 rows x 98 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.11710817, -0.1528703 , -0.0433963 , ..., -0.08703883,\n",
       "        -0.08703883, -0.10680283],\n",
       "       [-0.09396955, -0.145009  , -0.0433963 , ..., -0.08703883,\n",
       "        -0.08703883, -0.10680283],\n",
       "       [ 0.52494561,  4.48942416, -0.0433963 , ..., -0.08703883,\n",
       "        -0.08703883, -0.10680283],\n",
       "       ...,\n",
       "       [-0.09561336, -0.13217937, -0.0433963 , ..., -0.08703883,\n",
       "        -0.08703883, -0.10680283],\n",
       "       [-0.11694817, -0.15255998, -0.0433963 , ..., -0.08703883,\n",
       "        -0.08703883, -0.10680283],\n",
       "       [-0.11710536, -0.15285552, -0.0433963 , ..., -0.08703883,\n",
       "        -0.08703883, -0.10680283]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standardizing dataset so columns that contain larger values do not unduly influence the outcome\n",
    "X_scaled = StandardScaler().fit_transform(X)\n",
    "X_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(532, 98)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dimensionality Reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-3.35098808e-01,  1.03218907e+00, -5.90713438e-01, ...,\n",
       "        -1.26750423e-16,  1.25419852e-15,  6.60895243e-16],\n",
       "       [-3.18433948e-01,  1.03233143e+00, -5.91125551e-01, ...,\n",
       "        -1.89054534e-16,  1.68655252e-15, -7.28125125e-16],\n",
       "       [ 2.30546811e+00,  1.65638302e+00, -6.83616670e-01, ...,\n",
       "         3.69762716e-14,  2.20832022e-14, -3.51508568e-14],\n",
       "       ...,\n",
       "       [ 3.24348535e-01, -2.31230764e+00,  4.21515366e-01, ...,\n",
       "        -2.74775553e-14,  3.17615759e-14,  9.69693002e-15],\n",
       "       [-1.49363652e-01, -2.04933553e+00,  4.12968257e-01, ...,\n",
       "         3.76713466e-16, -5.25646697e-16,  3.16389250e-15],\n",
       "       [-2.89957500e-01,  8.21194511e-01, -2.76326847e-01, ...,\n",
       "         1.24152744e-15,  1.42174916e-15,  7.43414614e-16]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform dimensionality reduction with PCA\n",
    "pca = PCA(n_components=0.90)\n",
    "\n",
    "#Get principal components\n",
    "df_pca = pca.fit_transform(X_scaled)\n",
    "df_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(532, 74)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "      <th>0</th>\n",
       "      <td>-0.335099</td>\n",
       "      <td>1.032189</td>\n",
       "      <td>-0.590713</td>\n",
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       "      <td>-1.701841e-14</td>\n",
       "      <td>5.234900e-14</td>\n",
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       "      <td>-0.007129</td>\n",
       "      <td>-1.319756</td>\n",
       "      <td>-0.051813</td>\n",
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       "      <td>-4.260599e-15</td>\n",
       "      <td>-6.955336e-16</td>\n",
       "      <td>-2.492898e-15</td>\n",
       "      <td>2.410255e-15</td>\n",
       "      <td>-2.437666e-15</td>\n",
       "      <td>2.846442e-15</td>\n",
       "      <td>-1.728928e-15</td>\n",
       "      <td>-1.267504e-16</td>\n",
       "      <td>1.254199e-15</td>\n",
       "      <td>6.608952e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.318434</td>\n",
       "      <td>1.032331</td>\n",
       "      <td>-0.591126</td>\n",
       "      <td>0.001386</td>\n",
       "      <td>-1.587738e-14</td>\n",
       "      <td>5.138430e-14</td>\n",
       "      <td>2.671136e-12</td>\n",
       "      <td>-0.007739</td>\n",
       "      <td>-1.322514</td>\n",
       "      <td>-0.056674</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.714292e-15</td>\n",
       "      <td>-1.502294e-15</td>\n",
       "      <td>-2.369562e-15</td>\n",
       "      <td>2.850210e-15</td>\n",
       "      <td>-2.042657e-15</td>\n",
       "      <td>2.964025e-15</td>\n",
       "      <td>-2.112049e-15</td>\n",
       "      <td>-1.890545e-16</td>\n",
       "      <td>1.686553e-15</td>\n",
       "      <td>-7.281251e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.305468</td>\n",
       "      <td>1.656383</td>\n",
       "      <td>-0.683617</td>\n",
       "      <td>0.004731</td>\n",
       "      <td>-3.241252e-14</td>\n",
       "      <td>5.616447e-14</td>\n",
       "      <td>9.207736e-12</td>\n",
       "      <td>-0.054781</td>\n",
       "      <td>-1.542879</td>\n",
       "      <td>-0.943951</td>\n",
       "      <td>...</td>\n",
       "      <td>9.730983e-15</td>\n",
       "      <td>1.819229e-14</td>\n",
       "      <td>3.230119e-14</td>\n",
       "      <td>3.924040e-14</td>\n",
       "      <td>6.462610e-14</td>\n",
       "      <td>-6.033011e-14</td>\n",
       "      <td>4.569698e-14</td>\n",
       "      <td>3.697627e-14</td>\n",
       "      <td>2.208320e-14</td>\n",
       "      <td>-3.515086e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.145184</td>\n",
       "      <td>-1.320593</td>\n",
       "      <td>0.192813</td>\n",
       "      <td>-0.001229</td>\n",
       "      <td>9.268692e-15</td>\n",
       "      <td>-2.045732e-14</td>\n",
       "      <td>-2.395910e-12</td>\n",
       "      <td>-0.002071</td>\n",
       "      <td>0.281463</td>\n",
       "      <td>0.251862</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.421386e-15</td>\n",
       "      <td>-2.038938e-15</td>\n",
       "      <td>2.150009e-15</td>\n",
       "      <td>-1.075764e-14</td>\n",
       "      <td>-1.215486e-15</td>\n",
       "      <td>-2.808137e-14</td>\n",
       "      <td>-1.956127e-15</td>\n",
       "      <td>3.803804e-15</td>\n",
       "      <td>1.186330e-14</td>\n",
       "      <td>3.908188e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.151768</td>\n",
       "      <td>-2.036192</td>\n",
       "      <td>0.396182</td>\n",
       "      <td>-0.001705</td>\n",
       "      <td>9.405152e-15</td>\n",
       "      <td>-4.258007e-14</td>\n",
       "      <td>-3.309233e-12</td>\n",
       "      <td>0.027735</td>\n",
       "      <td>0.519099</td>\n",
       "      <td>0.188820</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.551320e-14</td>\n",
       "      <td>-2.091417e-14</td>\n",
       "      <td>-1.044086e-13</td>\n",
       "      <td>1.109509e-14</td>\n",
       "      <td>1.098861e-14</td>\n",
       "      <td>3.812324e-14</td>\n",
       "      <td>-7.503773e-14</td>\n",
       "      <td>-8.814406e-14</td>\n",
       "      <td>3.628828e-14</td>\n",
       "      <td>-7.632377e-14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2         3             4             5   \\\n",
       "0 -0.335099  1.032189 -0.590713  0.001397 -1.701841e-14  5.234900e-14   \n",
       "1 -0.318434  1.032331 -0.591126  0.001386 -1.587738e-14  5.138430e-14   \n",
       "2  2.305468  1.656383 -0.683617  0.004731 -3.241252e-14  5.616447e-14   \n",
       "3 -0.145184 -1.320593  0.192813 -0.001229  9.268692e-15 -2.045732e-14   \n",
       "4 -0.151768 -2.036192  0.396182 -0.001705  9.405152e-15 -4.258007e-14   \n",
       "\n",
       "             6         7         8         9   ...            64  \\\n",
       "0  2.693587e-12 -0.007129 -1.319756 -0.051813  ... -4.260599e-15   \n",
       "1  2.671136e-12 -0.007739 -1.322514 -0.056674  ... -4.714292e-15   \n",
       "2  9.207736e-12 -0.054781 -1.542879 -0.943951  ...  9.730983e-15   \n",
       "3 -2.395910e-12 -0.002071  0.281463  0.251862  ... -1.421386e-15   \n",
       "4 -3.309233e-12  0.027735  0.519099  0.188820  ... -1.551320e-14   \n",
       "\n",
       "             65            66            67            68            69  \\\n",
       "0 -6.955336e-16 -2.492898e-15  2.410255e-15 -2.437666e-15  2.846442e-15   \n",
       "1 -1.502294e-15 -2.369562e-15  2.850210e-15 -2.042657e-15  2.964025e-15   \n",
       "2  1.819229e-14  3.230119e-14  3.924040e-14  6.462610e-14 -6.033011e-14   \n",
       "3 -2.038938e-15  2.150009e-15 -1.075764e-14 -1.215486e-15 -2.808137e-14   \n",
       "4 -2.091417e-14 -1.044086e-13  1.109509e-14  1.098861e-14  3.812324e-14   \n",
       "\n",
       "             70            71            72            73  \n",
       "0 -1.728928e-15 -1.267504e-16  1.254199e-15  6.608952e-16  \n",
       "1 -2.112049e-15 -1.890545e-16  1.686553e-15 -7.281251e-16  \n",
       "2  4.569698e-14  3.697627e-14  2.208320e-14 -3.515086e-14  \n",
       "3 -1.956127e-15  3.803804e-15  1.186330e-14  3.908188e-15  \n",
       "4 -7.503773e-14 -8.814406e-14  3.628828e-14 -7.632377e-14  \n",
       "\n",
       "[5 rows x 74 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data frame\n",
    "df_pca = pd.DataFrame(data= df_pca)\n",
    "df_pca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.74246154, 2.1029187 , 2.01436892, 2.00754156, 2.00754005,\n",
       "       2.00754005, 2.00754005, 2.00547232, 1.8369474 , 1.72440686,\n",
       "       1.70949674, 1.59943293, 1.56717658, 1.52297788, 1.40895337,\n",
       "       1.29713801, 1.21413359, 1.18435602, 1.08424717, 1.05204614,\n",
       "       1.03003376, 1.02198993, 1.02022505, 1.01890094, 1.01661666,\n",
       "       1.01526166, 1.01091357, 1.0095665 , 1.00947326, 1.00947326,\n",
       "       1.00947323, 1.00930286, 1.00840538, 1.00769894, 1.00757175,\n",
       "       1.00713878, 1.00694666, 1.0064583 , 1.00631669, 1.00568376,\n",
       "       1.00566393, 1.00566393, 1.00559617, 1.00542785, 1.00509215,\n",
       "       1.00491444, 1.00442832, 1.00388029, 1.00377002, 1.00377002,\n",
       "       1.00377002, 1.00377002, 1.00377002, 1.00377002, 1.00377002,\n",
       "       1.00377002, 1.00377002, 1.00377002, 1.00377002, 1.00377002,\n",
       "       1.00377002, 1.00377002, 1.00377002, 1.00377002, 1.00377002,\n",
       "       1.00377002, 1.00377002, 1.00377002, 1.00377002, 1.00377002,\n",
       "       1.00377002, 1.00377002, 1.00377002, 1.00377002])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0279317 , 0.02141802, 0.02051615, 0.02044661, 0.0204466 ,\n",
       "       0.0204466 , 0.0204466 , 0.02042554, 0.01870913, 0.01756291,\n",
       "       0.01741106, 0.01629007, 0.01596154, 0.01551138, 0.01435005,\n",
       "       0.01321122, 0.01236583, 0.01206255, 0.01104295, 0.01071499,\n",
       "       0.01049079, 0.01040887, 0.01039089, 0.01037741, 0.01035414,\n",
       "       0.01034034, 0.01029605, 0.01028233, 0.01028139, 0.01028139,\n",
       "       0.01028138, 0.01027965, 0.01027051, 0.01026331, 0.01026202,\n",
       "       0.01025761, 0.01025565, 0.01025068, 0.01024924, 0.01024279,\n",
       "       0.01024259, 0.01024259, 0.0102419 , 0.01024018, 0.01023676,\n",
       "       0.01023495, 0.01023   , 0.01022442, 0.0102233 , 0.0102233 ,\n",
       "       0.0102233 , 0.0102233 , 0.0102233 , 0.0102233 , 0.0102233 ,\n",
       "       0.0102233 , 0.0102233 , 0.0102233 , 0.0102233 , 0.0102233 ,\n",
       "       0.0102233 , 0.0102233 , 0.0102233 , 0.0102233 , 0.0102233 ,\n",
       "       0.0102233 , 0.0102233 , 0.0102233 , 0.0102233 , 0.0102233 ,\n",
       "       0.0102233 , 0.0102233 , 0.0102233 , 0.0102233 ])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           42 Coin\n",
       "2           404Coin\n",
       "5         EliteCoin\n",
       "7           Bitcoin\n",
       "8          Ethereum\n",
       "           ...     \n",
       "1238         ZEPHYR\n",
       "1242        Gapcoin\n",
       "1245         Beldex\n",
       "1246        Horizen\n",
       "1247    BitcoinPlus\n",
       "Name: CoinName, Length: 532, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.32295243,  0.97019532, -0.56012408],\n",
       "       [-0.30633458,  0.97044543, -0.56051171],\n",
       "       [ 2.30037806,  1.7075017 , -0.65215216],\n",
       "       ...,\n",
       "       [ 0.31841439, -2.25175334,  0.39889449],\n",
       "       [-0.15933768, -2.02899994,  0.33368015],\n",
       "       [-0.28512265,  0.80791644, -0.25820522]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform dimensionality reduction with PCA\n",
    "pca_ = PCA(n_components=3)\n",
    "\n",
    "#Get principal components\n",
    "df_pca_ = pca_.fit_transform(X_scaled)\n",
    "df_pca_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(532, 3)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pca_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>64</th>\n",
       "      <th>65</th>\n",
       "      <th>66</th>\n",
       "      <th>67</th>\n",
       "      <th>68</th>\n",
       "      <th>69</th>\n",
       "      <th>70</th>\n",
       "      <th>71</th>\n",
       "      <th>72</th>\n",
       "      <th>73</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.335099</td>\n",
       "      <td>1.032189</td>\n",
       "      <td>-0.590713</td>\n",
       "      <td>0.001397</td>\n",
       "      <td>-1.701841e-14</td>\n",
       "      <td>5.234900e-14</td>\n",
       "      <td>2.693587e-12</td>\n",
       "      <td>-0.007129</td>\n",
       "      <td>-1.319756</td>\n",
       "      <td>-0.051813</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.260599e-15</td>\n",
       "      <td>-6.955336e-16</td>\n",
       "      <td>-2.492898e-15</td>\n",
       "      <td>2.410255e-15</td>\n",
       "      <td>-2.437666e-15</td>\n",
       "      <td>2.846442e-15</td>\n",
       "      <td>-1.728928e-15</td>\n",
       "      <td>-1.267504e-16</td>\n",
       "      <td>1.254199e-15</td>\n",
       "      <td>6.608952e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.318434</td>\n",
       "      <td>1.032331</td>\n",
       "      <td>-0.591126</td>\n",
       "      <td>0.001386</td>\n",
       "      <td>-1.587738e-14</td>\n",
       "      <td>5.138430e-14</td>\n",
       "      <td>2.671136e-12</td>\n",
       "      <td>-0.007739</td>\n",
       "      <td>-1.322514</td>\n",
       "      <td>-0.056674</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.714292e-15</td>\n",
       "      <td>-1.502294e-15</td>\n",
       "      <td>-2.369562e-15</td>\n",
       "      <td>2.850210e-15</td>\n",
       "      <td>-2.042657e-15</td>\n",
       "      <td>2.964025e-15</td>\n",
       "      <td>-2.112049e-15</td>\n",
       "      <td>-1.890545e-16</td>\n",
       "      <td>1.686553e-15</td>\n",
       "      <td>-7.281251e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.305468</td>\n",
       "      <td>1.656383</td>\n",
       "      <td>-0.683617</td>\n",
       "      <td>0.004731</td>\n",
       "      <td>-3.241252e-14</td>\n",
       "      <td>5.616447e-14</td>\n",
       "      <td>9.207736e-12</td>\n",
       "      <td>-0.054781</td>\n",
       "      <td>-1.542879</td>\n",
       "      <td>-0.943951</td>\n",
       "      <td>...</td>\n",
       "      <td>9.730983e-15</td>\n",
       "      <td>1.819229e-14</td>\n",
       "      <td>3.230119e-14</td>\n",
       "      <td>3.924040e-14</td>\n",
       "      <td>6.462610e-14</td>\n",
       "      <td>-6.033011e-14</td>\n",
       "      <td>4.569698e-14</td>\n",
       "      <td>3.697627e-14</td>\n",
       "      <td>2.208320e-14</td>\n",
       "      <td>-3.515086e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.145184</td>\n",
       "      <td>-1.320593</td>\n",
       "      <td>0.192813</td>\n",
       "      <td>-0.001229</td>\n",
       "      <td>9.268692e-15</td>\n",
       "      <td>-2.045732e-14</td>\n",
       "      <td>-2.395910e-12</td>\n",
       "      <td>-0.002071</td>\n",
       "      <td>0.281463</td>\n",
       "      <td>0.251862</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.421386e-15</td>\n",
       "      <td>-2.038938e-15</td>\n",
       "      <td>2.150009e-15</td>\n",
       "      <td>-1.075764e-14</td>\n",
       "      <td>-1.215486e-15</td>\n",
       "      <td>-2.808137e-14</td>\n",
       "      <td>-1.956127e-15</td>\n",
       "      <td>3.803804e-15</td>\n",
       "      <td>1.186330e-14</td>\n",
       "      <td>3.908188e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.151768</td>\n",
       "      <td>-2.036192</td>\n",
       "      <td>0.396182</td>\n",
       "      <td>-0.001705</td>\n",
       "      <td>9.405152e-15</td>\n",
       "      <td>-4.258007e-14</td>\n",
       "      <td>-3.309233e-12</td>\n",
       "      <td>0.027735</td>\n",
       "      <td>0.519099</td>\n",
       "      <td>0.188820</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.551320e-14</td>\n",
       "      <td>-2.091417e-14</td>\n",
       "      <td>-1.044086e-13</td>\n",
       "      <td>1.109509e-14</td>\n",
       "      <td>1.098861e-14</td>\n",
       "      <td>3.812324e-14</td>\n",
       "      <td>-7.503773e-14</td>\n",
       "      <td>-8.814406e-14</td>\n",
       "      <td>3.628828e-14</td>\n",
       "      <td>-7.632377e-14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 74 columns</p>\n",
       "</div>"
      ],
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       "         0         1         2         3             4             5   \\\n",
       "0 -0.335099  1.032189 -0.590713  0.001397 -1.701841e-14  5.234900e-14   \n",
       "1 -0.318434  1.032331 -0.591126  0.001386 -1.587738e-14  5.138430e-14   \n",
       "2  2.305468  1.656383 -0.683617  0.004731 -3.241252e-14  5.616447e-14   \n",
       "3 -0.145184 -1.320593  0.192813 -0.001229  9.268692e-15 -2.045732e-14   \n",
       "4 -0.151768 -2.036192  0.396182 -0.001705  9.405152e-15 -4.258007e-14   \n",
       "\n",
       "             6         7         8         9   ...            64  \\\n",
       "0  2.693587e-12 -0.007129 -1.319756 -0.051813  ... -4.260599e-15   \n",
       "1  2.671136e-12 -0.007739 -1.322514 -0.056674  ... -4.714292e-15   \n",
       "2  9.207736e-12 -0.054781 -1.542879 -0.943951  ...  9.730983e-15   \n",
       "3 -2.395910e-12 -0.002071  0.281463  0.251862  ... -1.421386e-15   \n",
       "4 -3.309233e-12  0.027735  0.519099  0.188820  ... -1.551320e-14   \n",
       "\n",
       "             65            66            67            68            69  \\\n",
       "0 -6.955336e-16 -2.492898e-15  2.410255e-15 -2.437666e-15  2.846442e-15   \n",
       "1 -1.502294e-15 -2.369562e-15  2.850210e-15 -2.042657e-15  2.964025e-15   \n",
       "2  1.819229e-14  3.230119e-14  3.924040e-14  6.462610e-14 -6.033011e-14   \n",
       "3 -2.038938e-15  2.150009e-15 -1.075764e-14 -1.215486e-15 -2.808137e-14   \n",
       "4 -2.091417e-14 -1.044086e-13  1.109509e-14  1.098861e-14  3.812324e-14   \n",
       "\n",
       "             70            71            72            73  \n",
       "0 -1.728928e-15 -1.267504e-16  1.254199e-15  6.608952e-16  \n",
       "1 -2.112049e-15 -1.890545e-16  1.686553e-15 -7.281251e-16  \n",
       "2  4.569698e-14  3.697627e-14  2.208320e-14 -3.515086e-14  \n",
       "3 -1.956127e-15  3.803804e-15  1.186330e-14  3.908188e-15  \n",
       "4 -7.503773e-14 -8.814406e-14  3.628828e-14 -7.632377e-14  \n",
       "\n",
       "[5 rows x 74 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data frame\n",
    "df_pca_ = pd.DataFrame(data= df_pca)\n",
    "df_pca_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.74246154, 2.1029187 , 2.01436892, 2.00754156, 2.00754005,\n",
       "       2.00754005, 2.00754005, 2.00547232, 1.8369474 , 1.72440686,\n",
       "       1.70949674, 1.59943293, 1.56717658, 1.52297788, 1.40895337,\n",
       "       1.29713801, 1.21413359, 1.18435602, 1.08424717, 1.05204614,\n",
       "       1.03003376, 1.02198993, 1.02022505, 1.01890094, 1.01661666,\n",
       "       1.01526166, 1.01091357, 1.0095665 , 1.00947326, 1.00947326,\n",
       "       1.00947323, 1.00930286, 1.00840538, 1.00769894, 1.00757175,\n",
       "       1.00713878, 1.00694666, 1.0064583 , 1.00631669, 1.00568376,\n",
       "       1.00566393, 1.00566393, 1.00559617, 1.00542785, 1.00509215,\n",
       "       1.00491444, 1.00442832, 1.00388029, 1.00377002, 1.00377002,\n",
       "       1.00377002, 1.00377002, 1.00377002, 1.00377002, 1.00377002,\n",
       "       1.00377002, 1.00377002, 1.00377002, 1.00377002, 1.00377002,\n",
       "       1.00377002, 1.00377002, 1.00377002, 1.00377002, 1.00377002,\n",
       "       1.00377002, 1.00377002, 1.00377002, 1.00377002, 1.00377002,\n",
       "       1.00377002, 1.00377002, 1.00377002, 1.00377002])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0279317 , 0.02141802, 0.02051615, 0.02044661, 0.0204466 ,\n",
       "       0.0204466 , 0.0204466 , 0.02042554, 0.01870913, 0.01756291,\n",
       "       0.01741106, 0.01629007, 0.01596154, 0.01551138, 0.01435005,\n",
       "       0.01321122, 0.01236583, 0.01206255, 0.01104295, 0.01071499,\n",
       "       0.01049079, 0.01040887, 0.01039089, 0.01037741, 0.01035414,\n",
       "       0.01034034, 0.01029605, 0.01028233, 0.01028139, 0.01028139,\n",
       "       0.01028138, 0.01027965, 0.01027051, 0.01026331, 0.01026202,\n",
       "       0.01025761, 0.01025565, 0.01025068, 0.01024924, 0.01024279,\n",
       "       0.01024259, 0.01024259, 0.0102419 , 0.01024018, 0.01023676,\n",
       "       0.01023495, 0.01023   , 0.01022442, 0.0102233 , 0.0102233 ,\n",
       "       0.0102233 , 0.0102233 , 0.0102233 , 0.0102233 , 0.0102233 ,\n",
       "       0.0102233 , 0.0102233 , 0.0102233 , 0.0102233 , 0.0102233 ,\n",
       "       0.0102233 , 0.0102233 , 0.0102233 , 0.0102233 , 0.0102233 ,\n",
       "       0.0102233 , 0.0102233 , 0.0102233 , 0.0102233 , 0.0102233 ,\n",
       "       0.0102233 , 0.0102233 , 0.0102233 , 0.0102233 ])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reduce the dataset dimensions with t-SNE and visually inspect the results\n",
    "# Initialize t-SNE model\n",
    "tsne = TSNE(learning_rate=35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reduce dimensions\n",
    "tsne_features = tsne.fit_transform(df_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -9.217034  , -11.7495165 ],\n",
       "       [-15.035162  , -15.889202  ],\n",
       "       [-21.528168  , -18.784996  ],\n",
       "       ...,\n",
       "       [-14.29004   ,  15.297745  ],\n",
       "       [ -4.5806346 ,  20.11478   ],\n",
       "       [ 12.319689  ,  -0.12017665]], dtype=float32)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsne_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(532, 2)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsne_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare to plot the dataset\n",
    "\n",
    "# The first column of transformed features\n",
    "df_pca['x'] = tsne_features[:,0]\n",
    "\n",
    "# The second column of transformed features\n",
    "df_pca['y'] = tsne_features[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": [
    "# Visualize the clusters\n",
    "plt.scatter(df_pca['x'], df_pca['y'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DubaiCoin          2\n",
       "CypherPunkCoin     1\n",
       "TerraCoin          1\n",
       "GoldCoin           1\n",
       "PesetaCoin         1\n",
       "                  ..\n",
       "Galilel            1\n",
       "BitConnect Coin    1\n",
       "Zilbercoin         1\n",
       "Bitcoin Green      1\n",
       "Crown Coin         1\n",
       "Name: CoinName, Length: 531, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare to plot the dataset\n",
    "\n",
    "# The first column of transformed features\n",
    "df_pca_['x'] = tsne_features[:,0]\n",
    "\n",
    "# The second column of transformed features\n",
    "df_pca_['y'] = tsne_features[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": [
    "# Visualize the clusters\n",
    "plt.scatter(df_pca_['x'], df_pca_['y'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": [
    "# Initialize K-means model\n",
    "inertia = []\n",
    "# Same as k = list(range(1, 11))\n",
    "k = [1,2,3,4,5,6,7,8,9,10]\n",
    "\n",
    "\n",
    "# Looking for the best k\n",
    "for i in k:\n",
    "    km = KMeans(n_clusters=i, random_state=0)\n",
    "    km.fit(df_pca)\n",
    "    km.predict\n",
    "    inertia.append(km.inertia_)\n",
    "\n",
    "# Define a DataFrame to plot the Elbow Curve using hvPlot\n",
    "elbow_data = {\"k\": k, \"inertia\": inertia}\n",
    "df_elbow = pd.DataFrame(elbow_data)\n",
    "\n",
    "plt.plot(df_elbow['k'], df_elbow['inertia'])\n",
    "plt.xticks(range(1,11))\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Inertia')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>k</th>\n",
       "      <th>inertia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>214303.773448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>137059.322862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>98640.008188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>77350.381816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>71454.827728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>66785.449908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>63291.806393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>60486.265416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>57479.355765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>55138.242063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    k        inertia\n",
       "0   1  214303.773448\n",
       "1   2  137059.322862\n",
       "2   3   98640.008188\n",
       "3   4   77350.381816\n",
       "4   5   71454.827728\n",
       "5   6   66785.449908\n",
       "6   7   63291.806393\n",
       "7   8   60486.265416\n",
       "8   9   57479.355765\n",
       "9  10   55138.242063"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_elbow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This DataFrame should include a new column containing the clusters found\n",
    "def get_clusters(k, data):\n",
    "    # Initialize the K-Means model\n",
    "    model = KMeans(n_clusters=k, random_state=0)\n",
    "\n",
    "    # Train the model\n",
    "    model.fit(data)\n",
    "\n",
    "    # Predict clusters\n",
    "    predictions = model.predict(data)\n",
    "\n",
    "    # Create return DataFrame with predicted clusters\n",
    "    data[\"class\"] = model.labels_\n",
    "\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cluster with the best value of k\n",
    "clusters = get_clusters(4,df_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>67</th>\n",
       "      <th>68</th>\n",
       "      <th>69</th>\n",
       "      <th>70</th>\n",
       "      <th>71</th>\n",
       "      <th>72</th>\n",
       "      <th>73</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.335099</td>\n",
       "      <td>1.032189</td>\n",
       "      <td>-0.590713</td>\n",
       "      <td>0.001397</td>\n",
       "      <td>-1.701841e-14</td>\n",
       "      <td>5.234900e-14</td>\n",
       "      <td>2.693587e-12</td>\n",
       "      <td>-0.007129</td>\n",
       "      <td>-1.319756</td>\n",
       "      <td>-0.051813</td>\n",
       "      <td>...</td>\n",
       "      <td>2.410255e-15</td>\n",
       "      <td>-2.437666e-15</td>\n",
       "      <td>2.846442e-15</td>\n",
       "      <td>-1.728928e-15</td>\n",
       "      <td>-1.267504e-16</td>\n",
       "      <td>1.254199e-15</td>\n",
       "      <td>6.608952e-16</td>\n",
       "      <td>-9.217034</td>\n",
       "      <td>-11.749516</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.318434</td>\n",
       "      <td>1.032331</td>\n",
       "      <td>-0.591126</td>\n",
       "      <td>0.001386</td>\n",
       "      <td>-1.587738e-14</td>\n",
       "      <td>5.138430e-14</td>\n",
       "      <td>2.671136e-12</td>\n",
       "      <td>-0.007739</td>\n",
       "      <td>-1.322514</td>\n",
       "      <td>-0.056674</td>\n",
       "      <td>...</td>\n",
       "      <td>2.850210e-15</td>\n",
       "      <td>-2.042657e-15</td>\n",
       "      <td>2.964025e-15</td>\n",
       "      <td>-2.112049e-15</td>\n",
       "      <td>-1.890545e-16</td>\n",
       "      <td>1.686553e-15</td>\n",
       "      <td>-7.281251e-16</td>\n",
       "      <td>-15.035162</td>\n",
       "      <td>-15.889202</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.305468</td>\n",
       "      <td>1.656383</td>\n",
       "      <td>-0.683617</td>\n",
       "      <td>0.004731</td>\n",
       "      <td>-3.241252e-14</td>\n",
       "      <td>5.616447e-14</td>\n",
       "      <td>9.207736e-12</td>\n",
       "      <td>-0.054781</td>\n",
       "      <td>-1.542879</td>\n",
       "      <td>-0.943951</td>\n",
       "      <td>...</td>\n",
       "      <td>3.924040e-14</td>\n",
       "      <td>6.462610e-14</td>\n",
       "      <td>-6.033011e-14</td>\n",
       "      <td>4.569698e-14</td>\n",
       "      <td>3.697627e-14</td>\n",
       "      <td>2.208320e-14</td>\n",
       "      <td>-3.515086e-14</td>\n",
       "      <td>-21.528168</td>\n",
       "      <td>-18.784996</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.145184</td>\n",
       "      <td>-1.320593</td>\n",
       "      <td>0.192813</td>\n",
       "      <td>-0.001229</td>\n",
       "      <td>9.268692e-15</td>\n",
       "      <td>-2.045732e-14</td>\n",
       "      <td>-2.395910e-12</td>\n",
       "      <td>-0.002071</td>\n",
       "      <td>0.281463</td>\n",
       "      <td>0.251862</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.075764e-14</td>\n",
       "      <td>-1.215486e-15</td>\n",
       "      <td>-2.808137e-14</td>\n",
       "      <td>-1.956127e-15</td>\n",
       "      <td>3.803804e-15</td>\n",
       "      <td>1.186330e-14</td>\n",
       "      <td>3.908188e-15</td>\n",
       "      <td>25.394314</td>\n",
       "      <td>12.834557</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.151768</td>\n",
       "      <td>-2.036192</td>\n",
       "      <td>0.396182</td>\n",
       "      <td>-0.001705</td>\n",
       "      <td>9.405152e-15</td>\n",
       "      <td>-4.258007e-14</td>\n",
       "      <td>-3.309233e-12</td>\n",
       "      <td>0.027735</td>\n",
       "      <td>0.519099</td>\n",
       "      <td>0.188820</td>\n",
       "      <td>...</td>\n",
       "      <td>1.109509e-14</td>\n",
       "      <td>1.098861e-14</td>\n",
       "      <td>3.812324e-14</td>\n",
       "      <td>-7.503773e-14</td>\n",
       "      <td>-8.814406e-14</td>\n",
       "      <td>3.628828e-14</td>\n",
       "      <td>-7.632377e-14</td>\n",
       "      <td>3.097009</td>\n",
       "      <td>18.344032</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>2.471251</td>\n",
       "      <td>0.822241</td>\n",
       "      <td>-0.071160</td>\n",
       "      <td>-0.008206</td>\n",
       "      <td>3.261624e-14</td>\n",
       "      <td>8.693263e-14</td>\n",
       "      <td>-1.629869e-11</td>\n",
       "      <td>-0.851713</td>\n",
       "      <td>2.041159</td>\n",
       "      <td>3.560713</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.451753e-14</td>\n",
       "      <td>-4.831165e-16</td>\n",
       "      <td>-2.897384e-14</td>\n",
       "      <td>-3.978536e-15</td>\n",
       "      <td>2.086013e-15</td>\n",
       "      <td>1.233139e-14</td>\n",
       "      <td>4.439598e-15</td>\n",
       "      <td>22.262491</td>\n",
       "      <td>12.695492</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>-0.333144</td>\n",
       "      <td>1.032071</td>\n",
       "      <td>-0.590740</td>\n",
       "      <td>0.001399</td>\n",
       "      <td>-1.700209e-14</td>\n",
       "      <td>5.270187e-14</td>\n",
       "      <td>2.698181e-12</td>\n",
       "      <td>-0.007154</td>\n",
       "      <td>-1.320125</td>\n",
       "      <td>-0.052460</td>\n",
       "      <td>...</td>\n",
       "      <td>2.618221e-15</td>\n",
       "      <td>-2.280305e-15</td>\n",
       "      <td>2.556896e-15</td>\n",
       "      <td>-1.591959e-15</td>\n",
       "      <td>1.401725e-16</td>\n",
       "      <td>9.962256e-16</td>\n",
       "      <td>6.107514e-16</td>\n",
       "      <td>-11.850870</td>\n",
       "      <td>-17.166218</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>0.324349</td>\n",
       "      <td>-2.312308</td>\n",
       "      <td>0.421515</td>\n",
       "      <td>-0.001319</td>\n",
       "      <td>8.432016e-15</td>\n",
       "      <td>-4.696282e-14</td>\n",
       "      <td>-2.552543e-12</td>\n",
       "      <td>0.017309</td>\n",
       "      <td>0.161077</td>\n",
       "      <td>-0.047787</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.743336e-14</td>\n",
       "      <td>-3.554623e-14</td>\n",
       "      <td>-2.756141e-14</td>\n",
       "      <td>-3.307820e-14</td>\n",
       "      <td>-2.747756e-14</td>\n",
       "      <td>3.176158e-14</td>\n",
       "      <td>9.696930e-15</td>\n",
       "      <td>-14.290040</td>\n",
       "      <td>15.297745</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>-0.149364</td>\n",
       "      <td>-2.049336</td>\n",
       "      <td>0.412968</td>\n",
       "      <td>-0.001745</td>\n",
       "      <td>1.447107e-14</td>\n",
       "      <td>-4.444065e-14</td>\n",
       "      <td>-3.385260e-12</td>\n",
       "      <td>0.028954</td>\n",
       "      <td>0.544389</td>\n",
       "      <td>0.233373</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.156364e-15</td>\n",
       "      <td>-9.386548e-17</td>\n",
       "      <td>-5.251461e-15</td>\n",
       "      <td>2.453018e-16</td>\n",
       "      <td>3.767135e-16</td>\n",
       "      <td>-5.256467e-16</td>\n",
       "      <td>3.163892e-15</td>\n",
       "      <td>-4.580635</td>\n",
       "      <td>20.114780</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>-0.289957</td>\n",
       "      <td>0.821195</td>\n",
       "      <td>-0.276327</td>\n",
       "      <td>0.000555</td>\n",
       "      <td>-9.663897e-15</td>\n",
       "      <td>3.311212e-14</td>\n",
       "      <td>1.066729e-12</td>\n",
       "      <td>-0.025215</td>\n",
       "      <td>0.956880</td>\n",
       "      <td>-0.767355</td>\n",
       "      <td>...</td>\n",
       "      <td>3.574398e-15</td>\n",
       "      <td>-4.495531e-15</td>\n",
       "      <td>1.707100e-15</td>\n",
       "      <td>-1.707100e-15</td>\n",
       "      <td>1.241527e-15</td>\n",
       "      <td>1.421749e-15</td>\n",
       "      <td>7.434146e-16</td>\n",
       "      <td>12.319689</td>\n",
       "      <td>-0.120177</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>532 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3             4             5  \\\n",
       "0   -0.335099  1.032189 -0.590713  0.001397 -1.701841e-14  5.234900e-14   \n",
       "1   -0.318434  1.032331 -0.591126  0.001386 -1.587738e-14  5.138430e-14   \n",
       "2    2.305468  1.656383 -0.683617  0.004731 -3.241252e-14  5.616447e-14   \n",
       "3   -0.145184 -1.320593  0.192813 -0.001229  9.268692e-15 -2.045732e-14   \n",
       "4   -0.151768 -2.036192  0.396182 -0.001705  9.405152e-15 -4.258007e-14   \n",
       "..        ...       ...       ...       ...           ...           ...   \n",
       "527  2.471251  0.822241 -0.071160 -0.008206  3.261624e-14  8.693263e-14   \n",
       "528 -0.333144  1.032071 -0.590740  0.001399 -1.700209e-14  5.270187e-14   \n",
       "529  0.324349 -2.312308  0.421515 -0.001319  8.432016e-15 -4.696282e-14   \n",
       "530 -0.149364 -2.049336  0.412968 -0.001745  1.447107e-14 -4.444065e-14   \n",
       "531 -0.289957  0.821195 -0.276327  0.000555 -9.663897e-15  3.311212e-14   \n",
       "\n",
       "                6         7         8         9  ...            67  \\\n",
       "0    2.693587e-12 -0.007129 -1.319756 -0.051813  ...  2.410255e-15   \n",
       "1    2.671136e-12 -0.007739 -1.322514 -0.056674  ...  2.850210e-15   \n",
       "2    9.207736e-12 -0.054781 -1.542879 -0.943951  ...  3.924040e-14   \n",
       "3   -2.395910e-12 -0.002071  0.281463  0.251862  ... -1.075764e-14   \n",
       "4   -3.309233e-12  0.027735  0.519099  0.188820  ...  1.109509e-14   \n",
       "..            ...       ...       ...       ...  ...           ...   \n",
       "527 -1.629869e-11 -0.851713  2.041159  3.560713  ... -1.451753e-14   \n",
       "528  2.698181e-12 -0.007154 -1.320125 -0.052460  ...  2.618221e-15   \n",
       "529 -2.552543e-12  0.017309  0.161077 -0.047787  ... -4.743336e-14   \n",
       "530 -3.385260e-12  0.028954  0.544389  0.233373  ... -4.156364e-15   \n",
       "531  1.066729e-12 -0.025215  0.956880 -0.767355  ...  3.574398e-15   \n",
       "\n",
       "               68            69            70            71            72  \\\n",
       "0   -2.437666e-15  2.846442e-15 -1.728928e-15 -1.267504e-16  1.254199e-15   \n",
       "1   -2.042657e-15  2.964025e-15 -2.112049e-15 -1.890545e-16  1.686553e-15   \n",
       "2    6.462610e-14 -6.033011e-14  4.569698e-14  3.697627e-14  2.208320e-14   \n",
       "3   -1.215486e-15 -2.808137e-14 -1.956127e-15  3.803804e-15  1.186330e-14   \n",
       "4    1.098861e-14  3.812324e-14 -7.503773e-14 -8.814406e-14  3.628828e-14   \n",
       "..            ...           ...           ...           ...           ...   \n",
       "527 -4.831165e-16 -2.897384e-14 -3.978536e-15  2.086013e-15  1.233139e-14   \n",
       "528 -2.280305e-15  2.556896e-15 -1.591959e-15  1.401725e-16  9.962256e-16   \n",
       "529 -3.554623e-14 -2.756141e-14 -3.307820e-14 -2.747756e-14  3.176158e-14   \n",
       "530 -9.386548e-17 -5.251461e-15  2.453018e-16  3.767135e-16 -5.256467e-16   \n",
       "531 -4.495531e-15  1.707100e-15 -1.707100e-15  1.241527e-15  1.421749e-15   \n",
       "\n",
       "               73          x          y  class  \n",
       "0    6.608952e-16  -9.217034 -11.749516      2  \n",
       "1   -7.281251e-16 -15.035162 -15.889202      2  \n",
       "2   -3.515086e-14 -21.528168 -18.784996      2  \n",
       "3    3.908188e-15  25.394314  12.834557      1  \n",
       "4   -7.632377e-14   3.097009  18.344032      3  \n",
       "..            ...        ...        ...    ...  \n",
       "527  4.439598e-15  22.262491  12.695492      1  \n",
       "528  6.107514e-16 -11.850870 -17.166218      2  \n",
       "529  9.696930e-15 -14.290040  15.297745      3  \n",
       "530  3.163892e-15  -4.580635  20.114780      3  \n",
       "531  7.434146e-16  12.319689  -0.120177      0  \n",
       "\n",
       "[532 rows x 77 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function for labeling cluster in a scatter plot\n",
    "def show_clusters(df):\n",
    "    plt.scatter(df[0], df[1], c=df['class'], alpha=0.7,marker='^')\n",
    "    plt.rcParams[\"figure.figsize\"] = (5, 12)\n",
    "    plt.xlabel('PC1')\n",
    "    plt.ylabel('PC2')\n",
    "    plt.colorbar()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 360x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_clusters(clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "class=%{marker.color}<br>0=%{x}<br>1=%{y}<br>2=%{z}<extra></extra>",
         "legendgroup": "2",
         "marker": {
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          "coloraxis": "coloraxis",
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "2",
         "scene": "scene",
         "showlegend": true,
         "type": "scatter3d",
         "x": [
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       "                            \n",
       "var gd = document.getElementById('9a2bbdf4-d96a-46c8-b4b7-666168ccf584');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3D scatter with clusters\n",
    "import plotly.express as px\n",
    "fig = px.scatter_3d(\n",
    "    clusters,\n",
    "    x=0,\n",
    "    y=1,\n",
    "    z=2,\n",
    "    color=\"class\",\n",
    "    symbol=\"class\",\n",
    "    width=800,\n",
    ")\n",
    "fig.update_layout(legend=dict(x=0, y=1))\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python [conda env:root] *",
   "language": "python",
   "name": "conda-root-py"
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