Crypto_Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required libraries and dependencies\n",
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price_change_percentage_24h</th>\n",
       "      <th>price_change_percentage_7d</th>\n",
       "      <th>price_change_percentage_14d</th>\n",
       "      <th>price_change_percentage_30d</th>\n",
       "      <th>price_change_percentage_60d</th>\n",
       "      <th>price_change_percentage_200d</th>\n",
       "      <th>price_change_percentage_1y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coin_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bitcoin</th>\n",
       "      <td>1.08388</td>\n",
       "      <td>7.60278</td>\n",
       "      <td>6.57509</td>\n",
       "      <td>7.67258</td>\n",
       "      <td>-3.25185</td>\n",
       "      <td>83.51840</td>\n",
       "      <td>37.51761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ethereum</th>\n",
       "      <td>0.22392</td>\n",
       "      <td>10.38134</td>\n",
       "      <td>4.80849</td>\n",
       "      <td>0.13169</td>\n",
       "      <td>-12.88890</td>\n",
       "      <td>186.77418</td>\n",
       "      <td>101.96023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tether</th>\n",
       "      <td>-0.21173</td>\n",
       "      <td>0.04935</td>\n",
       "      <td>0.00640</td>\n",
       "      <td>-0.04237</td>\n",
       "      <td>0.28037</td>\n",
       "      <td>-0.00542</td>\n",
       "      <td>0.01954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ripple</th>\n",
       "      <td>-0.37819</td>\n",
       "      <td>-0.60926</td>\n",
       "      <td>2.24984</td>\n",
       "      <td>0.23455</td>\n",
       "      <td>-17.55245</td>\n",
       "      <td>39.53888</td>\n",
       "      <td>-16.60193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bitcoin-cash</th>\n",
       "      <td>2.90585</td>\n",
       "      <td>17.09717</td>\n",
       "      <td>14.75334</td>\n",
       "      <td>15.74903</td>\n",
       "      <td>-13.71793</td>\n",
       "      <td>21.66042</td>\n",
       "      <td>14.49384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>binancecoin</th>\n",
       "      <td>2.10423</td>\n",
       "      <td>12.85511</td>\n",
       "      <td>6.80688</td>\n",
       "      <td>0.05865</td>\n",
       "      <td>36.33486</td>\n",
       "      <td>155.61937</td>\n",
       "      <td>69.69195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chainlink</th>\n",
       "      <td>-0.23935</td>\n",
       "      <td>20.69459</td>\n",
       "      <td>9.30098</td>\n",
       "      <td>-11.21747</td>\n",
       "      <td>-43.69522</td>\n",
       "      <td>403.22917</td>\n",
       "      <td>325.13186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cardano</th>\n",
       "      <td>0.00322</td>\n",
       "      <td>13.99302</td>\n",
       "      <td>5.55476</td>\n",
       "      <td>10.10553</td>\n",
       "      <td>-22.84776</td>\n",
       "      <td>264.51418</td>\n",
       "      <td>156.09756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>litecoin</th>\n",
       "      <td>-0.06341</td>\n",
       "      <td>6.60221</td>\n",
       "      <td>7.28931</td>\n",
       "      <td>1.21662</td>\n",
       "      <td>-17.23960</td>\n",
       "      <td>27.49919</td>\n",
       "      <td>-12.66408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bitcoin-cash-sv</th>\n",
       "      <td>0.92530</td>\n",
       "      <td>3.29641</td>\n",
       "      <td>-1.86656</td>\n",
       "      <td>2.88926</td>\n",
       "      <td>-24.87434</td>\n",
       "      <td>7.42562</td>\n",
       "      <td>93.73082</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 price_change_percentage_24h  price_change_percentage_7d  \\\n",
       "coin_id                                                                    \n",
       "bitcoin                              1.08388                     7.60278   \n",
       "ethereum                             0.22392                    10.38134   \n",
       "tether                              -0.21173                     0.04935   \n",
       "ripple                              -0.37819                    -0.60926   \n",
       "bitcoin-cash                         2.90585                    17.09717   \n",
       "binancecoin                          2.10423                    12.85511   \n",
       "chainlink                           -0.23935                    20.69459   \n",
       "cardano                              0.00322                    13.99302   \n",
       "litecoin                            -0.06341                     6.60221   \n",
       "bitcoin-cash-sv                      0.92530                     3.29641   \n",
       "\n",
       "                 price_change_percentage_14d  price_change_percentage_30d  \\\n",
       "coin_id                                                                     \n",
       "bitcoin                              6.57509                      7.67258   \n",
       "ethereum                             4.80849                      0.13169   \n",
       "tether                               0.00640                     -0.04237   \n",
       "ripple                               2.24984                      0.23455   \n",
       "bitcoin-cash                        14.75334                     15.74903   \n",
       "binancecoin                          6.80688                      0.05865   \n",
       "chainlink                            9.30098                    -11.21747   \n",
       "cardano                              5.55476                     10.10553   \n",
       "litecoin                             7.28931                      1.21662   \n",
       "bitcoin-cash-sv                     -1.86656                      2.88926   \n",
       "\n",
       "                 price_change_percentage_60d  price_change_percentage_200d  \\\n",
       "coin_id                                                                      \n",
       "bitcoin                             -3.25185                      83.51840   \n",
       "ethereum                           -12.88890                     186.77418   \n",
       "tether                               0.28037                      -0.00542   \n",
       "ripple                             -17.55245                      39.53888   \n",
       "bitcoin-cash                       -13.71793                      21.66042   \n",
       "binancecoin                         36.33486                     155.61937   \n",
       "chainlink                          -43.69522                     403.22917   \n",
       "cardano                            -22.84776                     264.51418   \n",
       "litecoin                           -17.23960                      27.49919   \n",
       "bitcoin-cash-sv                    -24.87434                       7.42562   \n",
       "\n",
       "                 price_change_percentage_1y  \n",
       "coin_id                                      \n",
       "bitcoin                            37.51761  \n",
       "ethereum                          101.96023  \n",
       "tether                              0.01954  \n",
       "ripple                            -16.60193  \n",
       "bitcoin-cash                       14.49384  \n",
       "binancecoin                        69.69195  \n",
       "chainlink                         325.13186  \n",
       "cardano                           156.09756  \n",
       "litecoin                          -12.66408  \n",
       "bitcoin-cash-sv                    93.73082  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data into a Pandas DataFrame and make the index the \"coin_id\" column.\n",
    "market_data_df = pd.read_csv(\"Resources/crypto_market_data.csv\", index_col=\"coin_id\")\n",
    "\n",
    "# Display sample data\n",
    "market_data_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price_change_percentage_24h</th>\n",
       "      <th>price_change_percentage_7d</th>\n",
       "      <th>price_change_percentage_14d</th>\n",
       "      <th>price_change_percentage_30d</th>\n",
       "      <th>price_change_percentage_60d</th>\n",
       "      <th>price_change_percentage_200d</th>\n",
       "      <th>price_change_percentage_1y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>41.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.269686</td>\n",
       "      <td>4.497147</td>\n",
       "      <td>0.185787</td>\n",
       "      <td>1.545693</td>\n",
       "      <td>-0.094119</td>\n",
       "      <td>236.537432</td>\n",
       "      <td>347.667956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.694793</td>\n",
       "      <td>6.375218</td>\n",
       "      <td>8.376939</td>\n",
       "      <td>26.344218</td>\n",
       "      <td>47.365803</td>\n",
       "      <td>435.225304</td>\n",
       "      <td>1247.842884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-13.527860</td>\n",
       "      <td>-6.094560</td>\n",
       "      <td>-18.158900</td>\n",
       "      <td>-34.705480</td>\n",
       "      <td>-44.822480</td>\n",
       "      <td>-0.392100</td>\n",
       "      <td>-17.567530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.608970</td>\n",
       "      <td>0.047260</td>\n",
       "      <td>-5.026620</td>\n",
       "      <td>-10.438470</td>\n",
       "      <td>-25.907990</td>\n",
       "      <td>21.660420</td>\n",
       "      <td>0.406170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.063410</td>\n",
       "      <td>3.296410</td>\n",
       "      <td>0.109740</td>\n",
       "      <td>-0.042370</td>\n",
       "      <td>-7.544550</td>\n",
       "      <td>83.905200</td>\n",
       "      <td>69.691950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.612090</td>\n",
       "      <td>7.602780</td>\n",
       "      <td>5.510740</td>\n",
       "      <td>4.578130</td>\n",
       "      <td>0.657260</td>\n",
       "      <td>216.177610</td>\n",
       "      <td>168.372510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.840330</td>\n",
       "      <td>20.694590</td>\n",
       "      <td>24.239190</td>\n",
       "      <td>140.795700</td>\n",
       "      <td>223.064370</td>\n",
       "      <td>2227.927820</td>\n",
       "      <td>7852.089700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       price_change_percentage_24h  price_change_percentage_7d  \\\n",
       "count                    41.000000                   41.000000   \n",
       "mean                     -0.269686                    4.497147   \n",
       "std                       2.694793                    6.375218   \n",
       "min                     -13.527860                   -6.094560   \n",
       "25%                      -0.608970                    0.047260   \n",
       "50%                      -0.063410                    3.296410   \n",
       "75%                       0.612090                    7.602780   \n",
       "max                       4.840330                   20.694590   \n",
       "\n",
       "       price_change_percentage_14d  price_change_percentage_30d  \\\n",
       "count                    41.000000                    41.000000   \n",
       "mean                      0.185787                     1.545693   \n",
       "std                       8.376939                    26.344218   \n",
       "min                     -18.158900                   -34.705480   \n",
       "25%                      -5.026620                   -10.438470   \n",
       "50%                       0.109740                    -0.042370   \n",
       "75%                       5.510740                     4.578130   \n",
       "max                      24.239190                   140.795700   \n",
       "\n",
       "       price_change_percentage_60d  price_change_percentage_200d  \\\n",
       "count                    41.000000                     41.000000   \n",
       "mean                     -0.094119                    236.537432   \n",
       "std                      47.365803                    435.225304   \n",
       "min                     -44.822480                     -0.392100   \n",
       "25%                     -25.907990                     21.660420   \n",
       "50%                      -7.544550                     83.905200   \n",
       "75%                       0.657260                    216.177610   \n",
       "max                     223.064370                   2227.927820   \n",
       "\n",
       "       price_change_percentage_1y  \n",
       "count                   41.000000  \n",
       "mean                   347.667956  \n",
       "std                   1247.842884  \n",
       "min                    -17.567530  \n",
       "25%                      0.406170  \n",
       "50%                     69.691950  \n",
       "75%                    168.372510  \n",
       "max                   7852.089700  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate summary statistics\n",
    "market_data_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "       [ 5.15453442e-01,  4.61843180e-01,  7.69975432e-01,\n",
       "         2.24045375e-01, -7.46741671e-02, -3.55053705e-01,\n",
       "        -2.51623389e-01],\n",
       "       [ 5.17582538e-02, -9.28380975e-01, -8.71917511e-01,\n",
       "         5.87817038e-02, -1.59249637e-01, -5.12070892e-01,\n",
       "        -2.65036119e-01],\n",
       "       [-5.20315039e-02, -4.57229014e-01,  3.25221197e-02,\n",
       "        -1.84488686e-01, -7.08090069e-02, -4.51982345e-01,\n",
       "        -2.62140444e-01],\n",
       "       [-2.17983910e-01, -8.49381348e-01,  2.97632101e-01,\n",
       "        -1.99820170e-01,  1.77312724e+00, -4.73610984e-02,\n",
       "        -1.19226165e-01],\n",
       "       [ 6.13385000e-02, -7.06668758e-01, -1.53208558e-02,\n",
       "        -5.86935760e-02,  4.01731582e-03, -5.50002649e-01,\n",
       "        -2.81963356e-01],\n",
       "       [ 2.59097324e-01,  2.49508456e-01, -4.78952718e-01,\n",
       "        -2.18996632e-01, -7.35815146e-01, -3.29179315e-01,\n",
       "        -2.85309811e-01],\n",
       "       [ 5.85088683e-01, -9.94231467e-01, -2.21710811e+00,\n",
       "        -6.03897825e-01, -9.30422964e-01,  7.71490396e-02,\n",
       "        -1.17481544e-01],\n",
       "       [-1.27467490e-01,  9.29118687e-01,  6.77532109e-01,\n",
       "         2.23834007e-01, -4.37067731e-01, -2.65163438e-01,\n",
       "        -2.14828585e-01],\n",
       "       [-1.61218775e+00, -1.68202656e+00, -8.16920931e-01,\n",
       "         1.14860683e+00,  1.71264110e+00,  1.50299207e+00,\n",
       "         2.86976907e-01],\n",
       "       [-2.96940167e-01,  9.47631360e-02,  4.00395074e-02,\n",
       "        -3.58829654e-01, -5.58527342e-01, -5.35731946e-01,\n",
       "        -2.84071416e-01],\n",
       "       [-7.13122190e-02, -2.29483757e-01, -1.75543942e-01,\n",
       "         5.18822668e-02, -5.51759945e-01, -5.26060334e-01,\n",
       "        -2.73061627e-01],\n",
       "       [-4.98104189e+00, -4.51782898e-02, -1.20695612e+00,\n",
       "        -1.21212587e+00,  4.77355426e-02,  4.63238025e+00,\n",
       "         6.08862545e+00],\n",
       "       [-1.25168231e-01,  5.80730108e-01, -2.02356361e-01,\n",
       "         5.82911022e-01, -3.95923001e-01, -3.58240047e-01,\n",
       "        -2.73433342e-01],\n",
       "       [-1.42857388e+00, -2.55101681e-02, -1.62885923e+00,\n",
       "        -8.60353737e-01, -8.40713540e-01,  8.98815486e-01,\n",
       "         2.68647377e-01],\n",
       "       [ 1.91981234e+00,  3.70447157e-01, -1.61976101e+00,\n",
       "        -4.09716316e-01,  1.69648044e+00,  8.11206969e-01,\n",
       "        -2.18878129e-02],\n",
       "       [ 1.04553034e+00, -6.18328162e-01,  2.90705393e+00,\n",
       "         5.35145461e+00,  4.76991278e+00,  3.14887546e+00,\n",
       "         1.34848839e+00],\n",
       "       [-4.09044076e-01, -9.06962812e-01, -1.29898610e+00,\n",
       "        -1.39315260e+00, -6.96936973e-01, -4.22834707e-01,\n",
       "        -2.92343790e-01],\n",
       "       [ 4.14710578e-01,  4.14044166e-01, -4.73857773e-02,\n",
       "        -4.65379896e-01,  1.28184655e-01, -4.16502012e-01,\n",
       "        -1.45469278e-01],\n",
       "       [ 7.80381841e-02, -6.87745436e-01, -9.19092169e-03,\n",
       "        -5.82143470e-02,  7.38829646e-03, -5.50440743e-01,\n",
       "        -2.81746818e-01],\n",
       "       [ 1.21745256e+00, -6.07713570e-01, -9.07065528e-01,\n",
       "         4.49939006e-01, -6.62529501e-01,  5.72366557e-01,\n",
       "        -1.32481673e-01]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the `StandardScaler()` module from scikit-learn to normalize the data from the CSV file\n",
    "crypto_data_scaled = StandardScaler().fit_transform(market_data_df[['price_change_percentage_24h', 'price_change_percentage_7d', \n",
    "                                                                    'price_change_percentage_14d',\t'price_change_percentage_30d',\t\n",
    "                                                                    'price_change_percentage_60d',\t'price_change_percentage_200d',\t\n",
    "                                                                    'price_change_percentage_1y']])\n",
    "\n",
    "display(crypto_data_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>price_change_percentage_24h</th>\n",
       "      <th>price_change_percentage_7d</th>\n",
       "      <th>price_change_percentage_14d</th>\n",
       "      <th>price_change_percentage_30d</th>\n",
       "      <th>price_change_percentage_60d</th>\n",
       "      <th>price_change_percentage_200d</th>\n",
       "      <th>price_change_percentage_1y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coin_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bitcoin</th>\n",
       "      <td>0.508529</td>\n",
       "      <td>0.493193</td>\n",
       "      <td>0.772200</td>\n",
       "      <td>0.235460</td>\n",
       "      <td>-0.067495</td>\n",
       "      <td>-0.355953</td>\n",
       "      <td>-0.251637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ethereum</th>\n",
       "      <td>0.185446</td>\n",
       "      <td>0.934445</td>\n",
       "      <td>0.558692</td>\n",
       "      <td>-0.054341</td>\n",
       "      <td>-0.273483</td>\n",
       "      <td>-0.115759</td>\n",
       "      <td>-0.199352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tether</th>\n",
       "      <td>0.021774</td>\n",
       "      <td>-0.706337</td>\n",
       "      <td>-0.021680</td>\n",
       "      <td>-0.061030</td>\n",
       "      <td>0.008005</td>\n",
       "      <td>-0.550247</td>\n",
       "      <td>-0.282061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ripple</th>\n",
       "      <td>-0.040764</td>\n",
       "      <td>-0.810928</td>\n",
       "      <td>0.249458</td>\n",
       "      <td>-0.050388</td>\n",
       "      <td>-0.373164</td>\n",
       "      <td>-0.458259</td>\n",
       "      <td>-0.295546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bitcoin-cash</th>\n",
       "      <td>1.193036</td>\n",
       "      <td>2.000959</td>\n",
       "      <td>1.760610</td>\n",
       "      <td>0.545842</td>\n",
       "      <td>-0.291203</td>\n",
       "      <td>-0.499848</td>\n",
       "      <td>-0.270317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>binancecoin</th>\n",
       "      <td>0.891871</td>\n",
       "      <td>1.327295</td>\n",
       "      <td>0.800214</td>\n",
       "      <td>-0.057148</td>\n",
       "      <td>0.778653</td>\n",
       "      <td>-0.188232</td>\n",
       "      <td>-0.225533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chainlink</th>\n",
       "      <td>0.011397</td>\n",
       "      <td>2.572251</td>\n",
       "      <td>1.101647</td>\n",
       "      <td>-0.490495</td>\n",
       "      <td>-0.931954</td>\n",
       "      <td>0.387759</td>\n",
       "      <td>-0.018284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cardano</th>\n",
       "      <td>0.102530</td>\n",
       "      <td>1.508001</td>\n",
       "      <td>0.648885</td>\n",
       "      <td>0.328959</td>\n",
       "      <td>-0.486349</td>\n",
       "      <td>0.065080</td>\n",
       "      <td>-0.155428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>litecoin</th>\n",
       "      <td>0.077497</td>\n",
       "      <td>0.334297</td>\n",
       "      <td>0.858520</td>\n",
       "      <td>-0.012646</td>\n",
       "      <td>-0.366477</td>\n",
       "      <td>-0.486266</td>\n",
       "      <td>-0.292351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bitcoin-cash-sv</th>\n",
       "      <td>0.448952</td>\n",
       "      <td>-0.190684</td>\n",
       "      <td>-0.248043</td>\n",
       "      <td>0.051634</td>\n",
       "      <td>-0.529666</td>\n",
       "      <td>-0.532961</td>\n",
       "      <td>-0.206029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 price_change_percentage_24h  price_change_percentage_7d  \\\n",
       "coin_id                                                                    \n",
       "bitcoin                             0.508529                    0.493193   \n",
       "ethereum                            0.185446                    0.934445   \n",
       "tether                              0.021774                   -0.706337   \n",
       "ripple                             -0.040764                   -0.810928   \n",
       "bitcoin-cash                        1.193036                    2.000959   \n",
       "binancecoin                         0.891871                    1.327295   \n",
       "chainlink                           0.011397                    2.572251   \n",
       "cardano                             0.102530                    1.508001   \n",
       "litecoin                            0.077497                    0.334297   \n",
       "bitcoin-cash-sv                     0.448952                   -0.190684   \n",
       "\n",
       "                 price_change_percentage_14d  price_change_percentage_30d  \\\n",
       "coin_id                                                                     \n",
       "bitcoin                             0.772200                     0.235460   \n",
       "ethereum                            0.558692                    -0.054341   \n",
       "tether                             -0.021680                    -0.061030   \n",
       "ripple                              0.249458                    -0.050388   \n",
       "bitcoin-cash                        1.760610                     0.545842   \n",
       "binancecoin                         0.800214                    -0.057148   \n",
       "chainlink                           1.101647                    -0.490495   \n",
       "cardano                             0.648885                     0.328959   \n",
       "litecoin                            0.858520                    -0.012646   \n",
       "bitcoin-cash-sv                    -0.248043                     0.051634   \n",
       "\n",
       "                 price_change_percentage_60d  price_change_percentage_200d  \\\n",
       "coin_id                                                                      \n",
       "bitcoin                            -0.067495                     -0.355953   \n",
       "ethereum                           -0.273483                     -0.115759   \n",
       "tether                              0.008005                     -0.550247   \n",
       "ripple                             -0.373164                     -0.458259   \n",
       "bitcoin-cash                       -0.291203                     -0.499848   \n",
       "binancecoin                         0.778653                     -0.188232   \n",
       "chainlink                          -0.931954                      0.387759   \n",
       "cardano                            -0.486349                      0.065080   \n",
       "litecoin                           -0.366477                     -0.486266   \n",
       "bitcoin-cash-sv                    -0.529666                     -0.532961   \n",
       "\n",
       "                 price_change_percentage_1y  \n",
       "coin_id                                      \n",
       "bitcoin                           -0.251637  \n",
       "ethereum                          -0.199352  \n",
       "tether                            -0.282061  \n",
       "ripple                            -0.295546  \n",
       "bitcoin-cash                      -0.270317  \n",
       "binancecoin                       -0.225533  \n",
       "chainlink                         -0.018284  \n",
       "cardano                           -0.155428  \n",
       "litecoin                          -0.292351  \n",
       "bitcoin-cash-sv                   -0.206029  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a DataFrame with the scaled data\n",
    "crypto_data_scaled_df = pd.DataFrame(crypto_data_scaled, \n",
    "                columns=['price_change_percentage_24h', 'price_change_percentage_7d', \n",
    "                        'price_change_percentage_14d', 'price_change_percentage_30d', \n",
    "                        'price_change_percentage_60d', 'price_change_percentage_200d', \n",
    "                        'price_change_percentage_1y'])\n",
    "\n",
    "# Copy the crypto names from the original data\n",
    "crypto_data_scaled_df['coin_id'] = market_data_df.index\n",
    "\n",
    "# Set the 'coin_id' column as the index\n",
    "crypto_data_scaled_df.set_index('coin_id', inplace=True)\n",
    "\n",
    "# Display sample data\n",
    "display(crypto_data_scaled_df.head(10))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the Best Value for k Using the Original Scaled DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\spenc\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\Users\\spenc\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\Users\\spenc\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\Users\\spenc\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\Users\\spenc\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\Users\\spenc\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\Users\\spenc\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\Users\\spenc\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    k     inertia\n",
      "0   1  287.000000\n",
      "1   2  198.571818\n",
      "2   3  123.190482\n",
      "3   4   79.022435\n",
      "4   5   68.243549\n",
      "5   6   61.096844\n",
      "6   7   50.255840\n",
      "7   8   40.888474\n",
      "8   9   32.768762\n",
      "9  10   29.090066\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\spenc\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\Users\\spenc\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Create a list with the number of k-values to try\n",
    "# Use a range from 1 to 11\n",
    "k = list(range(1, 11))\n",
    "\n",
    "# Create an empty list to store the inertia values\n",
    "inertia = []\n",
    "\n",
    "# Create a for loop to compute the inertia with each possible value of k\n",
    "# Inside the loop:\n",
    "# 1. Create a KMeans model using the loop counter for the n_clusters\n",
    "# 2. Fit the model to the data using the scaled DataFrame\n",
    "# 3. Append the model.inertia_ to the inertia list\n",
    "for i in k:\n",
    "    k_model = KMeans(n_clusters=i, n_init='auto', random_state=0)\n",
    "    k_model.fit(crypto_data_scaled_df)\n",
    "    inertia.append(k_model.inertia_)\n",
    "\n",
    "# Create a dictionary with the data to plot the Elbow curve\n",
    "elbow_data = {\n",
    "    'k': k,   \n",
    "    'inertia': inertia  \n",
    "}\n",
    "# print(elbow_data)\n",
    "\n",
    "# Create a DataFrame with the data to plot the Elbow curve\n",
    "elbow_df = pd.DataFrame(elbow_data)\n",
    "\n",
    "# Display the DataFrame\n",
    "print(elbow_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a line chart with all the inertia values computed with \n",
    "# the different values of k to visually identify the optimal value for k.\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plotting the Elbow curve\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.plot(elbow_df['k'], elbow_df['inertia'], marker='')\n",
    "plt.title('Elbow Curve')\n",
    "plt.xlabel('Number of Clusters (k)')\n",
    "plt.ylabel('Inertia')\n",
    "plt.xticks(elbow_df['k'])  # Set x-ticks to match k values\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Answer the following question: \n",
    "**Question:** What is the best value for `k`?\n",
    "\n",
    "**Answer:**  Value of 1 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster Cryptocurrencies with K-means Using the Original Scaled Data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the K-Means model using the best value for k\n",
    "model = KMeans(n_clusters=3, n_init='auto', random_state=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\spenc\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=3, n_init=&#x27;auto&#x27;, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=3, n_init=&#x27;auto&#x27;, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=3, n_init='auto', random_state=0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the K-Means model using the scaled data\n",
    "model.fit(crypto_data_scaled_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              price_change_percentage_24h  price_change_percentage_7d  \\\n",
      "coin_id                                                                 \n",
      "bitcoin                           1.08388                     7.60278   \n",
      "ethereum                          0.22392                    10.38134   \n",
      "tether                           -0.21173                     0.04935   \n",
      "ripple                           -0.37819                    -0.60926   \n",
      "bitcoin-cash                      2.90585                    17.09717   \n",
      "\n",
      "              price_change_percentage_14d  price_change_percentage_30d  \\\n",
      "coin_id                                                                  \n",
      "bitcoin                           6.57509                      7.67258   \n",
      "ethereum                          4.80849                      0.13169   \n",
      "tether                            0.00640                     -0.04237   \n",
      "ripple                            2.24984                      0.23455   \n",
      "bitcoin-cash                     14.75334                     15.74903   \n",
      "\n",
      "              price_change_percentage_60d  price_change_percentage_200d  \\\n",
      "coin_id                                                                   \n",
      "bitcoin                          -3.25185                      83.51840   \n",
      "ethereum                        -12.88890                     186.77418   \n",
      "tether                            0.28037                      -0.00542   \n",
      "ripple                          -17.55245                      39.53888   \n",
      "bitcoin-cash                    -13.71793                      21.66042   \n",
      "\n",
      "              price_change_percentage_1y  Cluster  \n",
      "coin_id                                            \n",
      "bitcoin                         37.51761        0  \n",
      "ethereum                       101.96023        0  \n",
      "tether                           0.01954        0  \n",
      "ripple                         -16.60193        0  \n",
      "bitcoin-cash                    14.49384        0  \n"
     ]
    }
   ],
   "source": [
    "# Ensure the DataFrame used for prediction does not have extra columns\n",
    "crypto_data_scaled_df = crypto_data_scaled_df[model.feature_names_in_]\n",
    "\n",
    "# Predict the clusters to group the cryptocurrencies using the scaled data\n",
    "cluster_assignments = model.predict(crypto_data_scaled_df)\n",
    "\n",
    "# Assuming `market_data_df` is your original DataFrame before scaling\n",
    "market_data_df['Cluster'] = cluster_assignments\n",
    "\n",
    "# View the resulting DataFrame with cluster assignments\n",
    "print(market_data_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a copy of the DataFrame\n",
    "crypto_data_copy = market_data_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      price_change_percentage_24h  price_change_percentage_7d  \\\n",
      "coin_id                                                                         \n",
      "bitcoin                                   1.08388                     7.60278   \n",
      "ethereum                                  0.22392                    10.38134   \n",
      "tether                                   -0.21173                     0.04935   \n",
      "ripple                                   -0.37819                    -0.60926   \n",
      "bitcoin-cash                              2.90585                    17.09717   \n",
      "binancecoin                               2.10423                    12.85511   \n",
      "chainlink                                -0.23935                    20.69459   \n",
      "cardano                                   0.00322                    13.99302   \n",
      "litecoin                                 -0.06341                     6.60221   \n",
      "bitcoin-cash-sv                           0.92530                     3.29641   \n",
      "crypto-com-chain                          0.61209                    -5.67151   \n",
      "usd-coin                                 -0.17825                    -0.11871   \n",
      "eos                                       0.14477                    -1.31177   \n",
      "monero                                    0.42961                    15.78515   \n",
      "tron                                      0.07647                     4.23886   \n",
      "tezos                                    -0.67316                     8.95665   \n",
      "okb                                      -2.72700                    -4.55389   \n",
      "stellar                                  -1.00843                     2.07149   \n",
      "cosmos                                   -0.95103                    16.08534   \n",
      "cdai                                      0.21169                     0.05820   \n",
      "neo                                       0.49302                     2.44243   \n",
      "wrapped-bitcoin                           1.10231                     7.40537   \n",
      "leo-token                                -0.13192                    -1.34886   \n",
      "huobi-token                              -0.40818                     1.61798   \n",
      "nem                                      -0.84990                    -0.85140   \n",
      "binance-usd                              -0.10642                     0.04726   \n",
      "iota                                      0.41996                     6.06830   \n",
      "vechain                                   1.28766                    -1.76352   \n",
      "zcash                                    -0.60897                    10.34780   \n",
      "theta-token                              -4.56089                    -6.09456   \n",
      "dash                                     -1.06006                     5.09387   \n",
      "ethereum-classic                         -0.45950                     3.05209   \n",
      "ethlend                                 -13.52786                     4.21266   \n",
      "maker                                    -0.60285                     8.15400   \n",
      "havven                                   -4.07216                     4.33651   \n",
      "omisego                                   4.84033                     6.82985   \n",
      "celsius-degree-token                      2.51323                     0.60354   \n",
      "ontology                                 -1.35845                    -1.21399   \n",
      "ftx-token                                 0.83416                     7.10438   \n",
      "true-usd                                 -0.06197                     0.16642   \n",
      "digibyte                                  2.97084                     0.67038   \n",
      "\n",
      "                      price_change_percentage_14d  \\\n",
      "coin_id                                             \n",
      "bitcoin                                   6.57509   \n",
      "ethereum                                  4.80849   \n",
      "tether                                    0.00640   \n",
      "ripple                                    2.24984   \n",
      "bitcoin-cash                             14.75334   \n",
      "binancecoin                               6.80688   \n",
      "chainlink                                 9.30098   \n",
      "cardano                                   5.55476   \n",
      "litecoin                                  7.28931   \n",
      "bitcoin-cash-sv                          -1.86656   \n",
      "crypto-com-chain                         -8.53948   \n",
      "usd-coin                                 -0.00568   \n",
      "eos                                       1.13751   \n",
      "monero                                   18.41097   \n",
      "tron                                      1.40337   \n",
      "tezos                                     2.32062   \n",
      "okb                                      -5.02662   \n",
      "stellar                                  -1.08217   \n",
      "cosmos                                    5.51074   \n",
      "cdai                                      0.17076   \n",
      "neo                                      -9.84803   \n",
      "wrapped-bitcoin                           6.55668   \n",
      "leo-token                                -7.02859   \n",
      "huobi-token                               0.45488   \n",
      "nem                                       2.64844   \n",
      "binance-usd                               0.05902   \n",
      "iota                                     -3.77714   \n",
      "vechain                                 -18.15890   \n",
      "zcash                                     5.79179   \n",
      "theta-token                              -6.57354   \n",
      "dash                                      0.51708   \n",
      "ethereum-classic                         -1.26669   \n",
      "ethlend                                  -9.80075   \n",
      "maker                                    -1.48854   \n",
      "havven                                  -13.29164   \n",
      "omisego                                 -13.21636   \n",
      "celsius-degree-token                     24.23919   \n",
      "ontology                                -10.56222   \n",
      "ftx-token                                -0.20629   \n",
      "true-usd                                  0.10974   \n",
      "digibyte                                 -7.31941   \n",
      "\n",
      "                      price_change_percentage_30d  \\\n",
      "coin_id                                             \n",
      "bitcoin                                   7.67258   \n",
      "ethereum                                  0.13169   \n",
      "tether                                   -0.04237   \n",
      "ripple                                    0.23455   \n",
      "bitcoin-cash                             15.74903   \n",
      "binancecoin                               0.05865   \n",
      "chainlink                               -11.21747   \n",
      "cardano                                  10.10553   \n",
      "litecoin                                  1.21662   \n",
      "bitcoin-cash-sv                           2.88926   \n",
      "crypto-com-chain                        -17.44782   \n",
      "usd-coin                                 -0.16584   \n",
      "eos                                      -4.63398   \n",
      "monero                                   38.95974   \n",
      "tron                                    -12.60389   \n",
      "tezos                                   -14.12663   \n",
      "okb                                     -10.43847   \n",
      "stellar                                  -8.12933   \n",
      "cosmos                                    4.57813   \n",
      "cdai                                     -2.18147   \n",
      "neo                                     -21.95472   \n",
      "wrapped-bitcoin                           7.37557   \n",
      "leo-token                                 3.07525   \n",
      "huobi-token                              -3.25488   \n",
      "nem                                      -3.65382   \n",
      "binance-usd                               0.01843   \n",
      "iota                                     -4.15281   \n",
      "vechain                                 -14.16831   \n",
      "zcash                                     7.37007   \n",
      "theta-token                              31.43355   \n",
      "dash                                     -7.79140   \n",
      "ethereum-classic                          2.89572   \n",
      "ethlend                                 -29.99499   \n",
      "maker                                    16.71360   \n",
      "havven                                  -20.84154   \n",
      "omisego                                  -9.11552   \n",
      "celsius-degree-token                    140.79570   \n",
      "ontology                                -34.70548   \n",
      "ftx-token                               -10.56394   \n",
      "true-usd                                  0.03090   \n",
      "digibyte                                 13.25354   \n",
      "\n",
      "                      price_change_percentage_60d  \\\n",
      "coin_id                                             \n",
      "bitcoin                                  -3.25185   \n",
      "ethereum                                -12.88890   \n",
      "tether                                    0.28037   \n",
      "ripple                                  -17.55245   \n",
      "bitcoin-cash                            -13.71793   \n",
      "binancecoin                              36.33486   \n",
      "chainlink                               -43.69522   \n",
      "cardano                                 -22.84776   \n",
      "litecoin                                -17.23960   \n",
      "bitcoin-cash-sv                         -24.87434   \n",
      "crypto-com-chain                        -16.47600   \n",
      "usd-coin                                  0.04271   \n",
      "eos                                     -30.16898   \n",
      "monero                                   41.72500   \n",
      "tron                                      5.52545   \n",
      "tezos                                   -44.82248   \n",
      "okb                                      -2.83120   \n",
      "stellar                                 -30.80369   \n",
      "cosmos                                   -7.20130   \n",
      "cdai                                      0.65726   \n",
      "neo                                      13.51879   \n",
      "wrapped-bitcoin                          -3.58772   \n",
      "leo-token                                -7.54455   \n",
      "huobi-token                              -3.40689   \n",
      "nem                                      82.86094   \n",
      "binance-usd                               0.09383   \n",
      "iota                                    -34.51894   \n",
      "vechain                                 -43.62359   \n",
      "zcash                                   -20.54216   \n",
      "theta-token                              80.03112   \n",
      "dash                                    -26.22460   \n",
      "ethereum-classic                        -25.90799   \n",
      "ethlend                                   2.13917   \n",
      "maker                                   -18.61722   \n",
      "havven                                  -39.42657   \n",
      "omisego                                  79.27505   \n",
      "celsius-degree-token                    223.06437   \n",
      "ontology                                -32.70004   \n",
      "ftx-token                                 5.90295   \n",
      "true-usd                                  0.25154   \n",
      "digibyte                                -31.09030   \n",
      "\n",
      "                      price_change_percentage_200d  \\\n",
      "coin_id                                              \n",
      "bitcoin                                   83.51840   \n",
      "ethereum                                 186.77418   \n",
      "tether                                    -0.00542   \n",
      "ripple                                    39.53888   \n",
      "bitcoin-cash                              21.66042   \n",
      "binancecoin                              155.61937   \n",
      "chainlink                                403.22917   \n",
      "cardano                                  264.51418   \n",
      "litecoin                                  27.49919   \n",
      "bitcoin-cash-sv                            7.42562   \n",
      "crypto-com-chain                         226.70782   \n",
      "usd-coin                                  -0.15691   \n",
      "eos                                       18.06111   \n",
      "monero                                   169.52147   \n",
      "tron                                     132.88436   \n",
      "tezos                                     43.42842   \n",
      "okb                                       39.95853   \n",
      "stellar                                   84.62157   \n",
      "cosmos                                   185.99786   \n",
      "cdai                                      -0.39210   \n",
      "neo                                      158.64773   \n",
      "wrapped-bitcoin                           83.90520   \n",
      "leo-token                                 16.40588   \n",
      "huobi-token                               42.23704   \n",
      "nem                                      216.17761   \n",
      "binance-usd                                0.09959   \n",
      "iota                                      95.02821   \n",
      "vechain                                  269.70264   \n",
      "zcash                                    122.54767   \n",
      "theta-token                              882.65105   \n",
      "dash                                       6.23435   \n",
      "ethereum-classic                          10.39203   \n",
      "ethlend                                 2227.92782   \n",
      "maker                                     82.53544   \n",
      "havven                                   622.92465   \n",
      "omisego                                  585.26307   \n",
      "celsius-degree-token                    1590.19149   \n",
      "ontology                                  54.76717   \n",
      "ftx-token                                 57.48950   \n",
      "true-usd                                  -0.08874   \n",
      "digibyte                                 482.58918   \n",
      "\n",
      "                      price_change_percentage_1y  Cluster  \n",
      "coin_id                                                    \n",
      "bitcoin                                 37.51761        0  \n",
      "ethereum                               101.96023        0  \n",
      "tether                                   0.01954        0  \n",
      "ripple                                 -16.60193        0  \n",
      "bitcoin-cash                            14.49384        0  \n",
      "binancecoin                             69.69195        0  \n",
      "chainlink                              325.13186        0  \n",
      "cardano                                156.09756        0  \n",
      "litecoin                               -12.66408        0  \n",
      "bitcoin-cash-sv                         93.73082        0  \n",
      "crypto-com-chain                       305.05908        0  \n",
      "usd-coin                                -0.19205        0  \n",
      "eos                                    -17.56753        0  \n",
      "monero                                 141.04116        0  \n",
      "tron                                    59.23821        0  \n",
      "tezos                                  140.01279        0  \n",
      "okb                                    141.95791        0  \n",
      "stellar                                 13.80715        0  \n",
      "cosmos                                  82.43833        0  \n",
      "cdai                                    -0.28783        0  \n",
      "neo                                    131.29655        0  \n",
      "wrapped-bitcoin                         37.53424        0  \n",
      "leo-token                               21.00263        0  \n",
      "huobi-token                             24.57164        0  \n",
      "nem                                    200.71797        0  \n",
      "binance-usd                              0.13928        0  \n",
      "iota                                    -3.98533        0  \n",
      "vechain                                202.86827        0  \n",
      "zcash                                   82.88499        0  \n",
      "theta-token                            701.37599        0  \n",
      "dash                                    -2.45897        0  \n",
      "ethereum-classic                        11.11094        0  \n",
      "ethlend                               7852.08970        1  \n",
      "maker                                   10.65279        0  \n",
      "havven                                 678.78427        0  \n",
      "omisego                                320.69054        0  \n",
      "celsius-degree-token                  2009.72217        2  \n",
      "ontology                               -12.65493        0  \n",
      "ftx-token                              168.37251        0  \n",
      "true-usd                                 0.40617        0  \n",
      "digibyte                               184.38014        0  \n"
     ]
    }
   ],
   "source": [
    "# Add a new column to the DataFrame with the predicted clusters\n",
    "crypto_data_copy['Cluster'] = cluster_assignments\n",
    "\n",
    "# Display the DataFrame with the new column\n",
    "print(crypto_data_copy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatter plot using Pandas plot by setting \n",
    "# `x=\"price_change_percentage_24h\"` and `y=\"price_change_percentage_7d\"`.\n",
    "# Use \"rainbow\" for the color to better visualize the data.\n",
    "crypto_plot = crypto_data_copy.plot.scatter(\n",
    "    x='price_change_percentage_24h',\n",
    "    y='price_change_percentage_7d',\n",
    "    c='Cluster',          \n",
    "    colormap='rainbow',\n",
    ")\n",
    "\n",
    "plt.title('Scatter Plot of Price Change Percentages')\n",
    "plt.xlabel('Price Change Percentage (24h)')\n",
    "plt.ylabel('Price Change Percentage (7d)')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimize Clusters with Principal Component Analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a PCA model instance and set `n_components=3`.\n",
    "pca = PCA(n_components=3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.60066733  0.84276006  0.46159457]\n",
      " [-0.45826071  0.45846566  0.95287678]\n",
      " [-0.43306981 -0.16812638 -0.64175193]\n",
      " [-0.47183495 -0.22266008 -0.47905316]\n",
      " [-1.15779997  2.04120919  1.85971527]]\n"
     ]
    }
   ],
   "source": [
    "# Use the PCA model with `fit_transform` on the original scaled DataFrame to reduce to three principal components.\n",
    "pca_data = pca.fit_transform(crypto_data_scaled_df)\n",
    "\n",
    "# View the first five rows of the DataFrame. \n",
    "print(pca_data[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.3719856 , 0.34700813, 0.17603793])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retrieve the explained variance to determine how much information  can be attributed to each principal component.\n",
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#explained_variance_ratios = [0.3719856, 0.34700813, 0.17603793]\n",
    "#total_explained_variance = sum(explained_variance_ratios)\n",
    "#print(total_explained_variance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Answer the following question: \n",
    "\n",
    "**Question:** What is the total explained variance of the three principal components?\n",
    "\n",
    "**Answer:** 0.89503166"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>PCA1</th>\n",
       "      <th>PCA2</th>\n",
       "      <th>PCA3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coin_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bitcoin</th>\n",
       "      <td>-0.600667</td>\n",
       "      <td>0.842760</td>\n",
       "      <td>0.461595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ethereum</th>\n",
       "      <td>-0.458261</td>\n",
       "      <td>0.458466</td>\n",
       "      <td>0.952877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tether</th>\n",
       "      <td>-0.433070</td>\n",
       "      <td>-0.168126</td>\n",
       "      <td>-0.641752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ripple</th>\n",
       "      <td>-0.471835</td>\n",
       "      <td>-0.222660</td>\n",
       "      <td>-0.479053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bitcoin-cash</th>\n",
       "      <td>-1.157800</td>\n",
       "      <td>2.041209</td>\n",
       "      <td>1.859715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>binancecoin</th>\n",
       "      <td>-0.516534</td>\n",
       "      <td>1.388377</td>\n",
       "      <td>0.804071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chainlink</th>\n",
       "      <td>-0.450711</td>\n",
       "      <td>0.517699</td>\n",
       "      <td>2.846143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cardano</th>\n",
       "      <td>-0.345600</td>\n",
       "      <td>0.729439</td>\n",
       "      <td>1.478013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>litecoin</th>\n",
       "      <td>-0.649468</td>\n",
       "      <td>0.432165</td>\n",
       "      <td>0.600303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bitcoin-cash-sv</th>\n",
       "      <td>-0.759014</td>\n",
       "      <td>-0.201200</td>\n",
       "      <td>-0.217653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crypto-com-chain</th>\n",
       "      <td>-0.248198</td>\n",
       "      <td>-1.376252</td>\n",
       "      <td>-1.462026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usd-coin</th>\n",
       "      <td>-0.438408</td>\n",
       "      <td>-0.175337</td>\n",
       "      <td>-0.663388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eos</th>\n",
       "      <td>-0.693425</td>\n",
       "      <td>-0.473815</td>\n",
       "      <td>-0.527597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monero</th>\n",
       "      <td>0.060499</td>\n",
       "      <td>2.909404</td>\n",
       "      <td>1.498571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tron</th>\n",
       "      <td>-0.393352</td>\n",
       "      <td>-0.108192</td>\n",
       "      <td>-0.012756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tezos</th>\n",
       "      <td>-0.796176</td>\n",
       "      <td>-0.494409</td>\n",
       "      <td>1.082812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>okb</th>\n",
       "      <td>0.064075</td>\n",
       "      <td>-1.269825</td>\n",
       "      <td>-1.098829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stellar</th>\n",
       "      <td>-0.489015</td>\n",
       "      <td>-0.732719</td>\n",
       "      <td>-0.062543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cosmos</th>\n",
       "      <td>-0.306272</td>\n",
       "      <td>0.703415</td>\n",
       "      <td>1.714224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cdai</th>\n",
       "      <td>-0.513528</td>\n",
       "      <td>-0.142802</td>\n",
       "      <td>-0.656566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neo</th>\n",
       "      <td>-0.362120</td>\n",
       "      <td>-0.986914</td>\n",
       "      <td>-0.728752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wrapped-bitcoin</th>\n",
       "      <td>-0.604265</td>\n",
       "      <td>0.827398</td>\n",
       "      <td>0.439316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>leo-token</th>\n",
       "      <td>-0.413296</td>\n",
       "      <td>-0.674115</td>\n",
       "      <td>-1.076628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>huobi-token</th>\n",
       "      <td>-0.407483</td>\n",
       "      <td>-0.212507</td>\n",
       "      <td>-0.351426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nem</th>\n",
       "      <td>0.608974</td>\n",
       "      <td>0.563532</td>\n",
       "      <td>-1.148742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>binance-usd</th>\n",
       "      <td>-0.450211</td>\n",
       "      <td>-0.151019</td>\n",
       "      <td>-0.647401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iota</th>\n",
       "      <td>-0.764665</td>\n",
       "      <td>-0.517886</td>\n",
       "      <td>0.204990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vechain</th>\n",
       "      <td>-0.556315</td>\n",
       "      <td>-1.938209</td>\n",
       "      <td>-1.261776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zcash</th>\n",
       "      <td>-0.425147</td>\n",
       "      <td>0.492976</td>\n",
       "      <td>1.058048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>theta-token</th>\n",
       "      <td>2.676868</td>\n",
       "      <td>-0.013954</td>\n",
       "      <td>-1.965207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dash</th>\n",
       "      <td>-0.613923</td>\n",
       "      <td>-0.479337</td>\n",
       "      <td>0.339565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ethereum-classic</th>\n",
       "      <td>-0.579924</td>\n",
       "      <td>-0.356334</td>\n",
       "      <td>-0.114942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ethlend</th>\n",
       "      <td>8.089018</td>\n",
       "      <td>-3.896891</td>\n",
       "      <td>2.301382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maker</th>\n",
       "      <td>-0.389045</td>\n",
       "      <td>0.165041</td>\n",
       "      <td>0.379414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>havven</th>\n",
       "      <td>0.865762</td>\n",
       "      <td>-2.261882</td>\n",
       "      <td>0.275583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>omisego</th>\n",
       "      <td>0.111675</td>\n",
       "      <td>0.428316</td>\n",
       "      <td>-1.205398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>celsius-degree-token</th>\n",
       "      <td>4.792395</td>\n",
       "      <td>6.767679</td>\n",
       "      <td>-1.986985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ontology</th>\n",
       "      <td>-0.632355</td>\n",
       "      <td>-2.108117</td>\n",
       "      <td>-0.652227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftx-token</th>\n",
       "      <td>-0.593142</td>\n",
       "      <td>0.021485</td>\n",
       "      <td>0.209911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>true-usd</th>\n",
       "      <td>-0.458131</td>\n",
       "      <td>-0.135734</td>\n",
       "      <td>-0.635284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>digibyte</th>\n",
       "      <td>-0.297910</td>\n",
       "      <td>-0.191126</td>\n",
       "      <td>-0.909602</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          PCA1      PCA2      PCA3\n",
       "coin_id                                           \n",
       "bitcoin              -0.600667  0.842760  0.461595\n",
       "ethereum             -0.458261  0.458466  0.952877\n",
       "tether               -0.433070 -0.168126 -0.641752\n",
       "ripple               -0.471835 -0.222660 -0.479053\n",
       "bitcoin-cash         -1.157800  2.041209  1.859715\n",
       "binancecoin          -0.516534  1.388377  0.804071\n",
       "chainlink            -0.450711  0.517699  2.846143\n",
       "cardano              -0.345600  0.729439  1.478013\n",
       "litecoin             -0.649468  0.432165  0.600303\n",
       "bitcoin-cash-sv      -0.759014 -0.201200 -0.217653\n",
       "crypto-com-chain     -0.248198 -1.376252 -1.462026\n",
       "usd-coin             -0.438408 -0.175337 -0.663388\n",
       "eos                  -0.693425 -0.473815 -0.527597\n",
       "monero                0.060499  2.909404  1.498571\n",
       "tron                 -0.393352 -0.108192 -0.012756\n",
       "tezos                -0.796176 -0.494409  1.082812\n",
       "okb                   0.064075 -1.269825 -1.098829\n",
       "stellar              -0.489015 -0.732719 -0.062543\n",
       "cosmos               -0.306272  0.703415  1.714224\n",
       "cdai                 -0.513528 -0.142802 -0.656566\n",
       "neo                  -0.362120 -0.986914 -0.728752\n",
       "wrapped-bitcoin      -0.604265  0.827398  0.439316\n",
       "leo-token            -0.413296 -0.674115 -1.076628\n",
       "huobi-token          -0.407483 -0.212507 -0.351426\n",
       "nem                   0.608974  0.563532 -1.148742\n",
       "binance-usd          -0.450211 -0.151019 -0.647401\n",
       "iota                 -0.764665 -0.517886  0.204990\n",
       "vechain              -0.556315 -1.938209 -1.261776\n",
       "zcash                -0.425147  0.492976  1.058048\n",
       "theta-token           2.676868 -0.013954 -1.965207\n",
       "dash                 -0.613923 -0.479337  0.339565\n",
       "ethereum-classic     -0.579924 -0.356334 -0.114942\n",
       "ethlend               8.089018 -3.896891  2.301382\n",
       "maker                -0.389045  0.165041  0.379414\n",
       "havven                0.865762 -2.261882  0.275583\n",
       "omisego               0.111675  0.428316 -1.205398\n",
       "celsius-degree-token  4.792395  6.767679 -1.986985\n",
       "ontology             -0.632355 -2.108117 -0.652227\n",
       "ftx-token            -0.593142  0.021485  0.209911\n",
       "true-usd             -0.458131 -0.135734 -0.635284\n",
       "digibyte             -0.297910 -0.191126 -0.909602"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a new DataFrame with the PCA data.\n",
    "# Note: The code for this step is provided for you\n",
    "\n",
    "# Creating a DataFrame with the PCA data\n",
    "df_crypto_pca = pd.DataFrame(pca_data, columns=[\"PCA1\", \"PCA2\", \"PCA3\"])\n",
    "\n",
    "# Copy the crypto names from the original data\n",
    "df_crypto_pca['coin_id'] = market_data_df.index\n",
    "\n",
    "# Set the coinid column as index\n",
    "df_crypto_pca.set_index('coin_id', inplace=True)\n",
    "\n",
    "# Display sample data\n",
    "display(df_crypto_pca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the Best Value for k Using the PCA Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\spenc\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\Users\\spenc\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\Users\\spenc\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\Users\\spenc\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\Users\\spenc\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\Users\\spenc\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\Users\\spenc\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\Users\\spenc\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\Users\\spenc\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'k': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 'inertia': [256.87408556789234, 168.81189630685213, 93.77462568057298, 49.665496651797355, 39.174438685096064, 32.45322891515885, 21.19403194710621, 19.50128617491428, 13.68113969299275, 12.44939020931306]}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\spenc\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Create a list with the number of k-values to try\n",
    "# Use a range from 1 to 11\n",
    "k = list(range(1,11))\n",
    "\n",
    "# Create an empty list to store the inertia values\n",
    "inertia = []\n",
    "\n",
    "# Create a for loop to compute the inertia with each possible value of k\n",
    "# Inside the loop:\n",
    "# 1. Create a KMeans model using the loop counter for the n_clusters\n",
    "# 2. Fit the model to the data using PCA DataFrame.\n",
    "# 3. Append the model.inertia_ to the inertia list\n",
    "for i in k:\n",
    "    k_model = KMeans(n_clusters=i, n_init='auto', random_state=0)\n",
    "    k_model.fit(df_crypto_pca)\n",
    "    inertia.append(k_model.inertia_)\n",
    "\n",
    "# Create a dictionary with the data to plot the Elbow curve\n",
    "elbow_data_PCA = {\n",
    "    'k': k,   \n",
    "    'inertia': inertia\n",
    "}\n",
    "\n",
    "# Create a DataFrame with the data to plot the Elbow curve\n",
    "elbow_df_PCA = pd.DataFrame(elbow_data_PCA)\n",
    "\n",
    "# Display the DataFrame\n",
    "print(elbow_data_PCA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a line chart with all the inertia values computed with \n",
    "# the different values of k to visually identify the optimal value for k.\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.plot(elbow_df_PCA['k'], elbow_df_PCA['inertia'], marker='')\n",
    "plt.title('Elbow Curve')\n",
    "plt.xlabel('Number of Clusters (k)')\n",
    "plt.ylabel('Inertia')\n",
    "plt.xticks(elbow_df_PCA['k'])\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Answer the following questions: \n",
    "* **Question:** What is the best value for `k` when using the PCA data?\n",
    "\n",
    "  * **Answer:** Value of 1\n",
    "\n",
    "\n",
    "* **Question:** Does it differ from the best k value found using the original data?\n",
    "\n",
    "  * **Answer:** Not sure /"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster Cryptocurrencies with K-means Using the PCA Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2\n",
      " 0 0 0 0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\spenc\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Initialize the K-Means model using the best value for k\n",
    "model1 = KMeans(n_clusters=3, n_init='auto', random_state=0)\n",
    "\n",
    "# Fit the K-Means model using the PCA data\n",
    "model1.fit(df_crypto_pca)\n",
    "\n",
    "# Predict the clusters to group the cryptocurrencies using the PCA data\n",
    "cluster_assignments = model1.predict(df_crypto_pca)\n",
    "\n",
    "# Print the resulting array of cluster values\n",
    "print(cluster_assignments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>PCA1</th>\n",
       "      <th>PCA2</th>\n",
       "      <th>PCA3</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coin_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bitcoin</th>\n",
       "      <td>-0.600667</td>\n",
       "      <td>0.842760</td>\n",
       "      <td>0.461595</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ethereum</th>\n",
       "      <td>-0.458261</td>\n",
       "      <td>0.458466</td>\n",
       "      <td>0.952877</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tether</th>\n",
       "      <td>-0.433070</td>\n",
       "      <td>-0.168126</td>\n",
       "      <td>-0.641752</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ripple</th>\n",
       "      <td>-0.471835</td>\n",
       "      <td>-0.222660</td>\n",
       "      <td>-0.479053</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bitcoin-cash</th>\n",
       "      <td>-1.157800</td>\n",
       "      <td>2.041209</td>\n",
       "      <td>1.859715</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>binancecoin</th>\n",
       "      <td>-0.516534</td>\n",
       "      <td>1.388377</td>\n",
       "      <td>0.804071</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chainlink</th>\n",
       "      <td>-0.450711</td>\n",
       "      <td>0.517699</td>\n",
       "      <td>2.846143</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cardano</th>\n",
       "      <td>-0.345600</td>\n",
       "      <td>0.729439</td>\n",
       "      <td>1.478013</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>litecoin</th>\n",
       "      <td>-0.649468</td>\n",
       "      <td>0.432165</td>\n",
       "      <td>0.600303</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bitcoin-cash-sv</th>\n",
       "      <td>-0.759014</td>\n",
       "      <td>-0.201200</td>\n",
       "      <td>-0.217653</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crypto-com-chain</th>\n",
       "      <td>-0.248198</td>\n",
       "      <td>-1.376252</td>\n",
       "      <td>-1.462026</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usd-coin</th>\n",
       "      <td>-0.438408</td>\n",
       "      <td>-0.175337</td>\n",
       "      <td>-0.663388</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eos</th>\n",
       "      <td>-0.693425</td>\n",
       "      <td>-0.473815</td>\n",
       "      <td>-0.527597</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monero</th>\n",
       "      <td>0.060499</td>\n",
       "      <td>2.909404</td>\n",
       "      <td>1.498571</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tron</th>\n",
       "      <td>-0.393352</td>\n",
       "      <td>-0.108192</td>\n",
       "      <td>-0.012756</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tezos</th>\n",
       "      <td>-0.796176</td>\n",
       "      <td>-0.494409</td>\n",
       "      <td>1.082812</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>okb</th>\n",
       "      <td>0.064075</td>\n",
       "      <td>-1.269825</td>\n",
       "      <td>-1.098829</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stellar</th>\n",
       "      <td>-0.489015</td>\n",
       "      <td>-0.732719</td>\n",
       "      <td>-0.062543</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cosmos</th>\n",
       "      <td>-0.306272</td>\n",
       "      <td>0.703415</td>\n",
       "      <td>1.714224</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cdai</th>\n",
       "      <td>-0.513528</td>\n",
       "      <td>-0.142802</td>\n",
       "      <td>-0.656566</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neo</th>\n",
       "      <td>-0.362120</td>\n",
       "      <td>-0.986914</td>\n",
       "      <td>-0.728752</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wrapped-bitcoin</th>\n",
       "      <td>-0.604265</td>\n",
       "      <td>0.827398</td>\n",
       "      <td>0.439316</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>leo-token</th>\n",
       "      <td>-0.413296</td>\n",
       "      <td>-0.674115</td>\n",
       "      <td>-1.076628</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>huobi-token</th>\n",
       "      <td>-0.407483</td>\n",
       "      <td>-0.212507</td>\n",
       "      <td>-0.351426</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nem</th>\n",
       "      <td>0.608974</td>\n",
       "      <td>0.563532</td>\n",
       "      <td>-1.148742</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>binance-usd</th>\n",
       "      <td>-0.450211</td>\n",
       "      <td>-0.151019</td>\n",
       "      <td>-0.647401</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iota</th>\n",
       "      <td>-0.764665</td>\n",
       "      <td>-0.517886</td>\n",
       "      <td>0.204990</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vechain</th>\n",
       "      <td>-0.556315</td>\n",
       "      <td>-1.938209</td>\n",
       "      <td>-1.261776</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zcash</th>\n",
       "      <td>-0.425147</td>\n",
       "      <td>0.492976</td>\n",
       "      <td>1.058048</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>theta-token</th>\n",
       "      <td>2.676868</td>\n",
       "      <td>-0.013954</td>\n",
       "      <td>-1.965207</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dash</th>\n",
       "      <td>-0.613923</td>\n",
       "      <td>-0.479337</td>\n",
       "      <td>0.339565</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ethereum-classic</th>\n",
       "      <td>-0.579924</td>\n",
       "      <td>-0.356334</td>\n",
       "      <td>-0.114942</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ethlend</th>\n",
       "      <td>8.089018</td>\n",
       "      <td>-3.896891</td>\n",
       "      <td>2.301382</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maker</th>\n",
       "      <td>-0.389045</td>\n",
       "      <td>0.165041</td>\n",
       "      <td>0.379414</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>havven</th>\n",
       "      <td>0.865762</td>\n",
       "      <td>-2.261882</td>\n",
       "      <td>0.275583</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>omisego</th>\n",
       "      <td>0.111675</td>\n",
       "      <td>0.428316</td>\n",
       "      <td>-1.205398</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>celsius-degree-token</th>\n",
       "      <td>4.792395</td>\n",
       "      <td>6.767679</td>\n",
       "      <td>-1.986985</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ontology</th>\n",
       "      <td>-0.632355</td>\n",
       "      <td>-2.108117</td>\n",
       "      <td>-0.652227</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftx-token</th>\n",
       "      <td>-0.593142</td>\n",
       "      <td>0.021485</td>\n",
       "      <td>0.209911</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>true-usd</th>\n",
       "      <td>-0.458131</td>\n",
       "      <td>-0.135734</td>\n",
       "      <td>-0.635284</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>digibyte</th>\n",
       "      <td>-0.297910</td>\n",
       "      <td>-0.191126</td>\n",
       "      <td>-0.909602</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          PCA1      PCA2      PCA3  Cluster\n",
       "coin_id                                                    \n",
       "bitcoin              -0.600667  0.842760  0.461595        0\n",
       "ethereum             -0.458261  0.458466  0.952877        0\n",
       "tether               -0.433070 -0.168126 -0.641752        0\n",
       "ripple               -0.471835 -0.222660 -0.479053        0\n",
       "bitcoin-cash         -1.157800  2.041209  1.859715        0\n",
       "binancecoin          -0.516534  1.388377  0.804071        0\n",
       "chainlink            -0.450711  0.517699  2.846143        0\n",
       "cardano              -0.345600  0.729439  1.478013        0\n",
       "litecoin             -0.649468  0.432165  0.600303        0\n",
       "bitcoin-cash-sv      -0.759014 -0.201200 -0.217653        0\n",
       "crypto-com-chain     -0.248198 -1.376252 -1.462026        0\n",
       "usd-coin             -0.438408 -0.175337 -0.663388        0\n",
       "eos                  -0.693425 -0.473815 -0.527597        0\n",
       "monero                0.060499  2.909404  1.498571        0\n",
       "tron                 -0.393352 -0.108192 -0.012756        0\n",
       "tezos                -0.796176 -0.494409  1.082812        0\n",
       "okb                   0.064075 -1.269825 -1.098829        0\n",
       "stellar              -0.489015 -0.732719 -0.062543        0\n",
       "cosmos               -0.306272  0.703415  1.714224        0\n",
       "cdai                 -0.513528 -0.142802 -0.656566        0\n",
       "neo                  -0.362120 -0.986914 -0.728752        0\n",
       "wrapped-bitcoin      -0.604265  0.827398  0.439316        0\n",
       "leo-token            -0.413296 -0.674115 -1.076628        0\n",
       "huobi-token          -0.407483 -0.212507 -0.351426        0\n",
       "nem                   0.608974  0.563532 -1.148742        0\n",
       "binance-usd          -0.450211 -0.151019 -0.647401        0\n",
       "iota                 -0.764665 -0.517886  0.204990        0\n",
       "vechain              -0.556315 -1.938209 -1.261776        0\n",
       "zcash                -0.425147  0.492976  1.058048        0\n",
       "theta-token           2.676868 -0.013954 -1.965207        0\n",
       "dash                 -0.613923 -0.479337  0.339565        0\n",
       "ethereum-classic     -0.579924 -0.356334 -0.114942        0\n",
       "ethlend               8.089018 -3.896891  2.301382        1\n",
       "maker                -0.389045  0.165041  0.379414        0\n",
       "havven                0.865762 -2.261882  0.275583        0\n",
       "omisego               0.111675  0.428316 -1.205398        0\n",
       "celsius-degree-token  4.792395  6.767679 -1.986985        2\n",
       "ontology             -0.632355 -2.108117 -0.652227        0\n",
       "ftx-token            -0.593142  0.021485  0.209911        0\n",
       "true-usd             -0.458131 -0.135734 -0.635284        0\n",
       "digibyte             -0.297910 -0.191126 -0.909602        0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a copy of the DataFrame with the PCA data\n",
    "df_crypto_pca_df = df_crypto_pca.copy()\n",
    "\n",
    "# Add a new column to the DataFrame with the predicted clusters\n",
    "df_crypto_pca_df['Cluster'] = cluster_assignments\n",
    "\n",
    "# Display sample data\n",
    "display(df_crypto_pca_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      var url = js_exports[name];\n      if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.holoviz.org/panel/1.3.8/dist/panel.min.js\"];\n  var js_modules = [];\n  var js_exports = {};\n  var css_urls = [];\n  var inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n\ttry {\n          inline_js[i].call(root, root.Bokeh);\n\t} catch(e) {\n\t  if (!reloading) {\n\t    throw e;\n\t  }\n\t}\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t  Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n      if (!reloading && !bokeh_loaded) {\n\troot.Bokeh = undefined;\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));",
      "application/vnd.holoviews_load.v0+json": ""
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            console.log(message)\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n        if (msg_handler) {\n          comm.on_msg(msg_handler);\n        }\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n        comm.open();\n        if (msg_handler) {\n          comm.onMsg = msg_handler;\n        }\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        var comm_promise = google.colab.kernel.comms.open(comm_id)\n        comm_promise.then((comm) => {\n          window.PyViz.comms[comm_id] = comm;\n          if (msg_handler) {\n            var messages = comm.messages[Symbol.asyncIterator]();\n            function processIteratorResult(result) {\n              var message = result.value;\n              var content = {data: message.data};\n              var metadata = message.metadata || {comm_id};\n              var msg = {content, metadata}\n              msg_handler(msg);\n              return messages.next().then(processIteratorResult);\n            }\n            return messages.next().then(processIteratorResult);\n          }\n        }) \n        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n          return comm_promise.then((comm) => {\n            comm.send(data, metadata, buffers, disposeOnDone);\n          });\n        };\n        var comm = {\n          send: sendClosure\n        };\n      }\n      window.PyViz.comms[comm_id] = comm;\n      return comm;\n    }\n    window.PyViz.comm_manager = new JupyterCommManager();\n    \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n  if (server_id !== null) {\n    comm.send({event_type: 'server_delete', 'id': server_id});\n    return;\n  } else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    EXEC_MIME_TYPE\n    );\n    this.keyboard_manager.register_events(toinsert);\n    // Render to node\n    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n",
      "application/vnd.holoviews_load.v0+json": ""
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>*[data-root-id],\n",
       "*[data-root-id] > * {\n",
       "  box-sizing: border-box;\n",
       "  font-family: var(--jp-ui-font-family);\n",
       "  font-size: var(--jp-ui-font-size1);\n",
       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
       "}\n",
       "\n",
       "/* Override VSCode background color */\n",
       ".cell-output-ipywidget-background:has(\n",
       "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
       "  ),\n",
       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
       "  background-color: transparent !important;\n",
       "}\n",
       "</style>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.holoviews_exec.v0+json": "",
      "text/html": [
       "<div id='p1002'>\n",
       "  <div id=\"fc8f3a51-1cb9-4643-95b8-703da34143a9\" data-root-id=\"p1002\" style=\"display: contents;\"></div>\n",
       "</div>\n",
       "<script type=\"application/javascript\">(function(root) {\n",
       "  var docs_json = {\"92b4806e-9b85-40c1-8d89-a983415c44e5\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"p1002\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"p1003\",\"attributes\":{\"plot_id\":\"p1002\",\"comm_id\":\"b6e67cd76066414c9a42360ac0310dc9\",\"client_comm_id\":\"ad891588fb3b4d26a97cc8a5a88081e5\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
       "  var render_items = [{\"docid\":\"92b4806e-9b85-40c1-8d89-a983415c44e5\",\"roots\":{\"p1002\":\"fc8f3a51-1cb9-4643-95b8-703da34143a9\"},\"root_ids\":[\"p1002\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
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       "\t  root_el.id = root_el.id + '-rendered'\n",
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fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
       "  var render_items = [{\"docid\":\"4f42aa79-2f88-485e-a2ee-6e3a42a9cb5f\",\"roots\":{\"p1004\":\"f8c9d4de-a047-4f94-aabf-d9bd67bac2b4\"},\"root_ids\":[\"p1004\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":Scatter   [PCA1]   (PCA2,Cluster)"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1004"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a scatter plot using hvPlot by setting `x=\"PCA1\"` and `y=\"PCA2\"`. \n",
    "import hvplot.pandas\n",
    "\n",
    "scatter_plot = df_crypto_pca_df.hvplot.scatter(\n",
    "    x='PCA1', \n",
    "    y='PCA2', \n",
    "    c='Cluster',  \n",
    "    cmap='viridis',  \n",
    "    title='PCA Scatter Plot with Clusters',\n",
    "    size=15,  \n",
    "    alpha=0.8  \n",
    ")\n",
    "scatter_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Determine the Weights of Each Feature on each Principal Component"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Answer the following question: \n",
    "\n",
    "* **Question:** Which features have the strongest positive or negative influence on each component? \n",
    " \n",
    "* **Answer:** PCA1 saw mostly losses, with major uptrend for Ethlend, Cel & THETA \n",
    "\n",
    "PCA2 had up trends on Cel, XMR & BNB\n",
    "\n",
    "PCA3 was ADA, BCH & LINK\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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