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       "  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>"
      ]
     },
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1002"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import required libraries and dependencies\n",
    "import pandas as pd\n",
    "import hvplot.pandas\n",
    "import matplotlib.pyplot as plt\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": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\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>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\n",
    "df_market_data = pd.read_csv(\n",
    "    \"Resources/crypto_market_data.csv\",\n",
    "    index_col=\"coin_id\")\n",
    "\n",
    "# Display sample data\n",
    "df_market_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead 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>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",
    "df_market_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
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       "  var render_items = [{\"docid\":\"2887ebd3-ac86-4f90-b457-b36b1b5486cb\",\"roots\":{\"p1004\":\"cdd4987e-7197-42e4-a9b3-9ea0777a05a0\"},\"root_ids\":[\"p1004\"]}];\n",
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       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
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       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
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       "          clearInterval(timer);\n",
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       "        }\n",
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       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":NdOverlay   [Variable]\n",
       "   :Curve   [coin_id]   (value)"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1004"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot your data to see what's in your DataFrame\n",
    "df_market_data.hvplot.line(\n",
    "    width=800,\n",
    "    height=400,\n",
    "    rot=90\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>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",
       "  </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",
       "\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",
       "\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",
       "\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  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the `StandardScaler()` module from scikit-learn to normalize the data from the CSV file\n",
    "# Identify numerical columns for scaling\n",
    "numerical_columns = df_market_data.select_dtypes(include=['float64', 'int64']).columns\n",
    "\n",
    "# Initialize the StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Normalize the data\n",
    "df_market_data[numerical_columns] = scaler.fit_transform(df_market_data[numerical_columns])\n",
    "\n",
    "df_market_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>crypto_name</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",
       "      <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",
       "      <td>bitcoin</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",
       "      <td>ethereum</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",
       "      <td>tether</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",
       "      <td>ripple</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",
       "      <td>bitcoin-cash</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",
       "\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",
       "\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",
       "\n",
       "              price_change_percentage_1y   crypto_name  \n",
       "coin_id                                                 \n",
       "bitcoin                        -0.251637       bitcoin  \n",
       "ethereum                       -0.199352      ethereum  \n",
       "tether                         -0.282061        tether  \n",
       "ripple                         -0.295546        ripple  \n",
       "bitcoin-cash                   -0.270317  bitcoin-cash  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame with the scaled data\n",
    "df_scaled = pd.DataFrame(df_market_data, columns=numerical_columns, index=df_market_data.index)\n",
    "\n",
    "# Copy the crypto names from the original data(already set while loading)\n",
    "df_scaled['crypto_name'] = df_market_data.index\n",
    "\n",
    "# Set the coinid column as index (Already set coin_id as index while loading)\n",
    "# df_scaled.set_index('coin_id', inplace=True)\n",
    "\n",
    "# # Explicitly reset and set the index (optional)\n",
    "# df_scaled.reset_index(inplace=True)\n",
    "# df_scaled.set_index('coin_id', inplace=True)\n",
    "\n",
    "# Display sample data\n",
    "df_scaled.head(5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the Best Value for k Using the Original Data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of k-values: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]\n"
     ]
    }
   ],
   "source": [
    "# Create a list with the number of k-values from 1 to 11\n",
    "k_values = list(range(1, 12))  # Range from 1 to 11 (exclusive)\n",
    "print(\"List of k-values:\", k_values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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/plain": [
       "[287.0,\n",
       " 212.1233420748626,\n",
       " 170.193570879703,\n",
       " 79.02243535120977,\n",
       " 71.0979135124442,\n",
       " 66.1088267804475,\n",
       " 54.48812315624117,\n",
       " 43.224692666580424,\n",
       " 32.86088447122681,\n",
       " 28.404884636873092,\n",
       " 24.116198421409198]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_market_data_scaled = scaler.fit_transform(df_market_data)\n",
    "# Create an empty list to store the inertia values\n",
    "inertia_values = []\n",
    "\n",
    "# Create a for loop to compute the inertia with each possible value of k\n",
    "for k in range(1, 12):  # Range from 1 to 11 (exclusive)\n",
    "# Inside the loop:\n",
    "# 1. Create a KMeans model using the loop counter for the n_clusters\n",
    "    kmean = KMeans(n_clusters=k, random_state=42)\n",
    "# 2. Fit the model to the data using `df_market_data_scaled`\n",
    "    kmean.fit(df_market_data_scaled)\n",
    "# 3. Append the model.inertia_ to the inertia list\n",
    "    inertia_values.append(kmean.inertia_)\n",
    "\n",
    "inertia_values\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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/plain": [
       "{1: 287.0,\n",
       " 2: 212.1233420748626,\n",
       " 3: 170.193570879703,\n",
       " 4: 79.02243535120977,\n",
       " 5: 71.0979135124442,\n",
       " 6: 66.1088267804475,\n",
       " 7: 54.48812315624117,\n",
       " 8: 43.224692666580424,\n",
       " 9: 32.86088447122681,\n",
       " 10: 28.404884636873092,\n",
       " 11: 24.116198421409198}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dictionary with the data to plot the Elbow curve\n",
    "elbow_data = {}\n",
    "# Create a for loop to compute inertia with each possible value of k\n",
    "for k in range(1, 12):  # Range from 1 to 11 (exclusive)\n",
    "    # Create KMeans model\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "    \n",
    "    # Fit the model to the scaled data\n",
    "    kmeans.fit(df_market_data_scaled)\n",
    "    \n",
    "    # Store inertia value in the dictionary\n",
    "    elbow_data[k] = kmeans.inertia_\n",
    "\n",
    "# Display the Elbow curve data dictionary\n",
    "elbow_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Number of Clusters</th>\n",
       "      <th>Inertia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>287.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>212.123342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>170.193571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>79.022435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>71.097914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>66.108827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>54.488123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>43.224693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>32.860884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>28.404885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>24.116198</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Number of Clusters     Inertia\n",
       "0                    1  287.000000\n",
       "1                    2  212.123342\n",
       "2                    3  170.193571\n",
       "3                    4   79.022435\n",
       "4                    5   71.097914\n",
       "5                    6   66.108827\n",
       "6                    7   54.488123\n",
       "7                    8   43.224693\n",
       "8                    9   32.860884\n",
       "9                   10   28.404885\n",
       "10                  11   24.116198"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert dictionary to a DataFrame for plotting\n",
    "df_elbow = pd.DataFrame(list(elbow_data.items()), columns=['Number of Clusters', 'Inertia'])\n",
    "\n",
    "# Display the DataFrame for the Elbow curve data\n",
    "df_elbow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a line chart with all the inertia values computed with \n",
    "# Plotting the Elbow curve\n",
    "# the different values of k to visually identify the optimal value for k.\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(range(1, 12), inertia_values, marker='o', linestyle='-', color='b')\n",
    "plt.title('Elbow Method For Optimal k')\n",
    "plt.xlabel('Number of Clusters (k)')\n",
    "plt.ylabel('Inertia')\n",
    "plt.xticks(range(1, 12))\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Answer the following question: \n",
    "\n",
    "**Question:** What is the best value for `k`?\n",
    "\n",
    "**Answer:** \n",
    "To determine the best value for 𝑘\n",
    "k (number of clusters), typically we look for the point where the decrease in inertia (or within-cluster sum of squares) starts to level off significantly. In your provided data:\n",
    "\n",
    "Number of Clusters (k) ranges from 1 to 11.\n",
    "Inertia decreases as the number of clusters increases.\n",
    "The best value for 𝑘\n",
    "k can often be identified using the \"elbow method\" or by examining the rate of decrease in inertia. From the data:\n",
    "\n",
    "As \n",
    "𝑘 increases, inertia decreases steeply from 287.0 (for 𝑘 = 1) down to 54.5 (for 𝑘 = 7). \n",
    "Beyond 𝑘 = 7, the decrease in inertia continues, but at a decreasing rate.\n",
    "\n",
    "The \"elbow\" point, where the rate of decrease in inertia slows down significantly, is around \n",
    "𝑘 = 7 or 𝑘 = 8\n",
    "Therefore, based on this inertia data, the best value for \n",
    "k appears to be 7 or 8.\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster Cryptocurrencies with K-means Using the Original Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the K-Means model using the best value for k\n",
    "k = 7  # or k = 8\n",
    "kmeans = KMeans(n_clusters=k, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=7, random_state=42)</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KMeans<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KMeans(n_clusters=7, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=7, random_state=42)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the K-Means model using the scaled data\n",
    "kmeans.fit(df_market_data_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 2, 2, 5, 4, 4, 4, 4, 2, 2, 2, 2, 5, 2, 0, 2, 0, 4, 2, 2, 4,\n",
       "       2, 2, 2, 2, 0, 2, 4, 6, 0, 0, 3, 4, 0, 2, 1, 2, 4, 2, 2])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict the clusters to group the cryptocurrencies using the scaled data\n",
    "cluster_predictions = kmeans.predict(df_market_data_scaled)\n",
    "\n",
    "# Print the resulting array of cluster values.\n",
    "cluster_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>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",
       "      <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",
       "      <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",
       "      <td>4</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",
       "      <td>4</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",
       "      <td>2</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",
       "      <td>2</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",
       "      <td>5</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",
       "\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",
       "\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",
       "\n",
       "              price_change_percentage_1y  Cluster  \n",
       "coin_id                                            \n",
       "bitcoin                        -0.251637        4  \n",
       "ethereum                       -0.199352        4  \n",
       "tether                         -0.282061        2  \n",
       "ripple                         -0.295546        2  \n",
       "bitcoin-cash                   -0.270317        5  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get cluster labels\n",
    "cluster_labels = kmeans.labels_\n",
    "\n",
    "# Assign cluster labels back to your original DataFrame if needed\n",
    "df_market_data['Cluster'] = cluster_labels\n",
    "\n",
    "# Optionally, you can also get centroids of clusters\n",
    "cluster_centers = kmeans.cluster_centers_\n",
    "df_market_data.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a copy of the DataFrame\n",
    "df_market_data_copy = df_market_data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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",
       "      <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",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>bitcoin</th>\n",
       "      <td>0.508529</td>\n",
       "      <td>0.493193</td>\n",
       "      <td>0.772200</td>\n",
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       "      <td>-0.067495</td>\n",
       "      <td>-0.355953</td>\n",
       "      <td>-0.251637</td>\n",
       "      <td>4</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",
       "      <td>4</td>\n",
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       "    <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",
       "      <td>2</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",
       "      <td>2</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",
       "      <td>5</td>\n",
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      ],
      "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",
       "\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",
       "\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",
       "\n",
       "              price_change_percentage_1y  Cluster  \n",
       "coin_id                                            \n",
       "bitcoin                        -0.251637        4  \n",
       "ethereum                       -0.199352        4  \n",
       "tether                         -0.282061        2  \n",
       "ripple                         -0.295546        2  \n",
       "bitcoin-cash                   -0.270317        5  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a new column to the DataFrame with the predicted clusters\n",
    "df_market_data_copy['Cluster'] = cluster_predictions\n",
    "\n",
    "# Display sample data\n",
    "df_market_data_copy.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
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       "  var render_items = [{\"docid\":\"827d7148-ee91-4fde-88a4-228ea55dbe94\",\"roots\":{\"p1176\":\"d329cb3e-8d65-4c1d-aca4-9b1b54954fb5\"},\"root_ids\":[\"p1176\"]}];\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   [price_change_percentage_24h]   (price_change_percentage_7d,Cluster,coin_id)"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1176"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a scatter plot using hvPlot by setting \n",
    "# `x=\"price_change_percentage_24h\"` and `y=\"price_change_percentage_7d\"`. \n",
    "# Color the graph points with the labels found using K-Means and \n",
    "# add the crypto name/coinID in the `hover_cols` parameter to identify \n",
    "# the cryptocurrency represented by each data point.\n",
    "scatter_plot = df_market_data_copy.hvplot.scatter(\n",
    "    x='price_change_percentage_24h',\n",
    "    y='price_change_percentage_7d',\n",
    "    c='Cluster',  # Color points by 'Cluster' column\n",
    "    colormap='viridis',  # Choose a colormap\n",
    "    hover_cols=['coin_id'],  # Show 'crypto_name' in hover info\n",
    "    xlabel='24h Price Change (%)',\n",
    "    ylabel='7d Price Change (%)',\n",
    "    title='Cryptocurrency Clustering'\n",
    ")\n",
    "\n",
    "# Display the plot\n",
    "scatter_plot\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimize Clusters with Principal Component Analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained Variance Ratio: [0.3719856  0.34700813 0.17603793]\n",
      "Cumulative Explained Variance Ratio: [0.3719856  0.71899373 0.89503166]\n"
     ]
    }
   ],
   "source": [
    "# Create a PCA model instance and set `n_components=3`.\n",
    "# Step 1: Standardize the Data\n",
    "scaler = StandardScaler()\n",
    "numerical_columns = df_market_data_copy.select_dtypes(include=['float64', 'int64']).columns\n",
    "df_scaled = scaler.fit_transform(df_market_data_copy[numerical_columns])\n",
    "\n",
    "# Step 2: Create PCA model instance with n_components=3\n",
    "pca = PCA(n_components=3)  # Set n_components=3 for three principal components\n",
    "principal_components = pca.fit_transform(df_scaled)\n",
    "\n",
    "# Print explained variance ratio\n",
    "explained_variance = pca.explained_variance_ratio_\n",
    "print(f\"Explained Variance Ratio: {explained_variance}\")\n",
    "\n",
    "# Optionally, you can also print the cumulative explained variance ratio\n",
    "cumulative_variance = explained_variance.cumsum()\n",
    "print(f\"Cumulative Explained Variance Ratio: {cumulative_variance}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   PC1       PC2       PC3\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"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the PCA model with `fit_transform` to reduce to \n",
    "# three principal components.\n",
    "df_pca = pd.DataFrame(data=principal_components, columns=['PC1', 'PC2', 'PC3'], index=df_market_data_copy.index)\n",
    "\n",
    "\n",
    "# View the first five rows of the DataFrame. \n",
    "df_pca.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained Variance for PC1: 0.372\n",
      "Explained Variance for PC2: 0.347\n",
      "Explained Variance for PC3: 0.176\n",
      "Cumulative Explained Variance Ratio: 0.895\n"
     ]
    }
   ],
   "source": [
    "# Retrieve the explained variance to determine how much information \n",
    "# can be attributed to each principal component.\n",
    "explained_variance = pca.explained_variance_ratio_\n",
    "# Display explained variance ratio for each principal component\n",
    "for i, ev in enumerate(explained_variance):\n",
    "    print(f\"Explained Variance for PC{i+1}: {ev:.3f}\")\n",
    "\n",
    "# Optionally, you can also print the cumulative explained variance ratio\n",
    "cumulative_variance = explained_variance.cumsum()\n",
    "print(f\"Cumulative Explained Variance Ratio: {cumulative_variance[-1]:.3f}\")\n"
   ]
  },
  {
   "attachments": {},
   "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:** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['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', 'Cluster'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df_market_data_copy.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   PC1       PC2       PC3\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"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new DataFrame with the PCA data.\n",
    "df_pca = pd.DataFrame(data=principal_components, columns=['PC1', 'PC2', 'PC3'], index=df_market_data_copy.index)\n",
    "# Creating a DataFrame with the PCA data\n",
    "# Set the coinid column as index (Already set)\n",
    "df_pca.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>coin_id</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>bitcoin</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>ethereum</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>tether</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>ripple</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>bitcoin-cash</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   PC1       PC2       PC3       coin_id\n",
       "coin_id                                                 \n",
       "bitcoin      -0.600667  0.842760  0.461595       bitcoin\n",
       "ethereum     -0.458261  0.458466  0.952877      ethereum\n",
       "tether       -0.433070 -0.168126 -0.641752        tether\n",
       "ripple       -0.471835 -0.222660 -0.479053        ripple\n",
       "bitcoin-cash -1.157800  2.041209  1.859715  bitcoin-cash"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Copy crypto names from original data to df_pca\n",
    "df_pca['coin_id'] = df_market_data_copy.index\n",
    "df_pca.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the Best Value for k Using the PCA Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               PC1       PC2       PC3\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"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pca_numerical = df_pca.drop(columns=['coin_id'], errors='ignore')\n",
    "df_pca_numerical.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list with the number of k-values from 1 to 11\n",
    "k_values = list(range(1, 12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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 an empty list to store the inertia values\n",
    "inertia_values = []\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 `df_market_data_pca`\n",
    "# 3. Append the model.inertia_ to the inertia list\n",
    "for k in k_values:\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "    kmeans.fit(df_pca_numerical)  # Use PCA-transformed numerical data for clustering\n",
    "    inertia_values.append(kmeans.inertia_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Number of Clusters</th>\n",
       "      <th>Inertia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>256.874086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>182.339530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>140.431833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>49.665497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>42.051901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>31.749005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>28.031869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>19.451747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>13.814082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>10.630648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>10.120243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Number of Clusters     Inertia\n",
       "0                    1  256.874086\n",
       "1                    2  182.339530\n",
       "2                    3  140.431833\n",
       "3                    4   49.665497\n",
       "4                    5   42.051901\n",
       "5                    6   31.749005\n",
       "6                    7   28.031869\n",
       "7                    8   19.451747\n",
       "8                    9   13.814082\n",
       "9                   10   10.630648\n",
       "10                  11   10.120243"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dictionary with the data to plot the Elbow curve\n",
    "elbow_data = {\n",
    "    'Number of Clusters': k_values,\n",
    "    'Inertia': inertia_values\n",
    "}\n",
    "\n",
    "# Convert the dictionary to a DataFrame\n",
    "df_elbow_1 = pd.DataFrame(elbow_data)\n",
    "df_elbow_1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 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",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(df_elbow_1['Number of Clusters'], df_elbow['Inertia'], marker='o')\n",
    "plt.xlabel('Number of Clusters (k)')\n",
    "plt.ylabel('Inertia')\n",
    "plt.title('Elbow Curve for Optimal k using PCA Data')\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Answer the following questions: \n",
    "\n",
    "* **Question:** What is the best value for `k` when using the PCA data?\n",
    "\n",
    "  * **Answer:**\n",
    "  To determine the best value for k, we look at the elbow curve, which plots the number of clusters against the inertia values. The \"elbow\" point, where the rate of decrease in inertia slows down significantly, is typically considered the optimal number of clusters.\n",
    "  The significant drop in inertia occurs at k = 4, where the inertia value drops from 140.431833 to 49.665497. After k = 4, the rate of decrease slows down.\n",
    "\n",
    "Therefore, the best value for k when using the PCA data appears to be 4.\n",
    "\n",
    "\n",
    "* **Question:** Does it differ from the best k value found using the original data?\n",
    "\n",
    "  * **Answer:** \n",
    "  Based on the original inertia values provided:\n",
    "  The significant drop in inertia using the original data occurs at k = 3, where the inertia value drops from 212.123342 to 170.193571. After k = 3, the rate of decrease slows down.\n",
    "\n",
    "Therefore, the best value for k when using the original data is 3.\n",
    "\n",
    "# comparison:\n",
    "Yes, the best value for k differs when using the PCA data compared to the original data:\n",
    "\n",
    "- Original data: The best k value is 3.\n",
    "- PCA data: The best k value is 4.\n",
    "This difference suggests that PCA transformation has an impact on the clustering structure of the data, potentially revealing different underlying patterns.\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster Cryptocurrencies with K-means Using the PCA Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the K-Means model using the best value for k\n",
    "k = 4\n",
    "kmeans_pca = KMeans(n_clusters=k, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ranar\\.conda\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: 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-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=4, random_state=42)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KMeans<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KMeans(n_clusters=4, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=4, random_state=42)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the K-Means model using the PCA data\n",
    "df_pca_numerical = df_pca[['PC1', 'PC2', 'PC3']]\n",
    "kmeans_pca.fit(df_pca_numerical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 2, 2, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 2, 0, 2, 2, 0, 2, 2, 0,\n",
       "       2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 3, 0, 2, 2, 1, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict the clusters to group the cryptocurrencies using the PCA data\n",
    "pca_clusters = kmeans_pca.predict(df_pca_numerical)\n",
    "\n",
    "# Print the resulting array of cluster values.\n",
    "pca_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>coin_id</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",
       "      <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>bitcoin</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>ethereum</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>tether</td>\n",
       "      <td>2</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>ripple</td>\n",
       "      <td>2</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>bitcoin-cash</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   PC1       PC2       PC3       coin_id  Cluster\n",
       "coin_id                                                          \n",
       "bitcoin      -0.600667  0.842760  0.461595       bitcoin        0\n",
       "ethereum     -0.458261  0.458466  0.952877      ethereum        0\n",
       "tether       -0.433070 -0.168126 -0.641752        tether        2\n",
       "ripple       -0.471835 -0.222660 -0.479053        ripple        2\n",
       "bitcoin-cash -1.157800  2.041209  1.859715  bitcoin-cash        0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a copy of the DataFrame with the PCA data\n",
    "df_pca_copy = df_pca.copy()\n",
    "\n",
    "# Add a new column to the DataFrame with the predicted clusters\n",
    "df_pca_copy['Cluster'] = pca_clusters\n",
    "\n",
    "# Display sample data\n",
    "df_pca_copy.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
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      "text/plain": [
       ":NdOverlay   [Cluster]\n",
       "   :Scatter   [PC1]   (PC2,coin_id)"
      ]
     },
     "execution_count": 34,
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   "source": [
    "# Create a scatter plot using hvPlot by setting \n",
    "# `x=\"PC1\"` and `y=\"PC2\"`. \n",
    "# Color the graph points with the labels found using K-Means and \n",
    "# add the crypto name in the `hover_cols` parameter to identify \n",
    "# the cryptocurrency represented by each data point.\n",
    "scatter_plot = df_pca_copy.hvplot.scatter(\n",
    "    x='PC1',\n",
    "    y='PC2',\n",
    "    by='Cluster',  # Color the points by the 'Cluster' column\n",
    "    hover_cols=['coin_id'],  # Include 'coin_id' in the hover information\n",
    "    title='PCA Scatter Plot with K-Means Clusters'\n",
    ")\n",
    "\n",
    "scatter_plot"
   ]
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   "cell_type": "markdown",
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   "source": [
    "### Visualize and Compare the Results\n",
    "\n",
    "In this section, you will visually analyze the cluster analysis results by contrasting the outcome with and without using the optimization techniques."
   ]
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   "cell_type": "code",
   "execution_count": 35,
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     "execution_count": 35,
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    "# Composite plot to contrast the Elbow curves\n",
    "plt.figure(figsize=(14, 7))\n"
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      "text/plain": [
       ":Layout\n",
       "   .Curve.I  :Curve   [Number of Clusters]   (Inertia)\n",
       "   .Curve.II :Curve   [Number of Clusters]   (Inertia)"
      ]
     },
     "execution_count": 36,
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   "source": [
    "# Composite plot for elbow curves\n",
    "elbow_original_plot = df_elbow.hvplot.line(x='Number of Clusters', y='Inertia', title='Elbow Curve - Original Data')\n",
    "elbow_pca_plot = df_elbow_1.hvplot.line(x='Number of Clusters', y='Inertia', title='Elbow Curve - PCA Data')\n",
    "\n",
    "# Combine plots using the plus operator\n",
    "composite_elbow_plot = elbow_original_plot + elbow_pca_plot\n",
    "\n",
    "# Display the composite plot\n",
    "composite_elbow_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
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      "text/plain": [
       ":Layout\n",
       "   .Scatter.I   :Scatter   [price_change_percentage_24h]   (price_change_percentage_7d,Cluster,coin_id)\n",
       "   .NdOverlay.I :NdOverlay   [Cluster]\n",
       "      :Scatter   [PC1]   (PC2,coin_id)"
      ]
     },
     "execution_count": 37,
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      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1494"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import hvplot.pandas\n",
    "\n",
    "# Scatter plot for original data\n",
    "scatter_original = df_market_data_copy.hvplot.scatter(\n",
    "    x='price_change_percentage_24h',\n",
    "    y='price_change_percentage_7d',\n",
    "    c='Cluster',            # Color points by 'Cluster' column\n",
    "    cmap='viridis',         # Choose a colormap\n",
    "    hover_cols=['coin_id'], # Show 'coin_id' in hover info\n",
    "    xlabel='24h Price Change (%)',\n",
    "    ylabel='7d Price Change (%)',\n",
    "    title='Cryptocurrency Clustering - Original Data'\n",
    ")\n",
    "\n",
    "# Scatter plot for PCA-transformed data\n",
    "scatter_pca = df_pca_copy.hvplot.scatter(\n",
    "    x='PC1',\n",
    "    y='PC2',\n",
    "    by='Cluster',           # Color points by 'Cluster' column\n",
    "    cmap='viridis',         # Choose a colormap\n",
    "    hover_cols=['coin_id'], # Show 'coin_id' in hover info\n",
    "    xlabel='PC1',\n",
    "    ylabel='PC2',\n",
    "    title='Cryptocurrency Clustering - PCA Data'\n",
    ")\n",
    "\n",
    "# Combine plots using the plus operator\n",
    "composite_cluster_plot = scatter_original + scatter_pca\n",
    "\n",
    "# Display the composite plot\n",
    "composite_cluster_plot\n"
   ]
  },
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   "source": [
    "#### Answer the following question: \n",
    "\n",
    "  * **Question:** After visually analyzing the cluster analysis results, what is the impact of using fewer features to cluster the data using K-Means?\n",
    "    * **Answer:** \n",
    "  In short, using fewer features through PCA for clustering using K-Means:\n",
    "\n",
    "Simplifies Data Representation: PCA reduces the complexity by transforming original features into principal components.\n",
    "\n",
    "Enhances Interpretability: It focuses on the most significant components, aiding in clearer visualization and understanding of data clusters.\n",
    "\n",
    "Potential Accuracy Trade-offs: There may be a trade-off with accuracy as PCA reduces some information from the original dataset.\n",
    "\n",
    "Improved Cluster Visualization: PCA can improve cluster separation in visualizations like scatter plots, making patterns more discernible.\n",
    "\n",
    "Overall, while PCA simplifies analysis and enhances interpretability, it's crucial to consider its impact on accuracy and ensure it aligns with specific analysis goals.\n",
    "\n"
   ]
  }
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