notebooks/crypto_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dependencies\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CoinName</th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>IsTrading</th>\n",
       "      <th>ProofType</th>\n",
       "      <th>TotalCoinsMined</th>\n",
       "      <th>TotalCoinSupply</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42 Coin</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>True</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>4.199995e+01</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>365Coin</td>\n",
       "      <td>X11</td>\n",
       "      <td>True</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2300000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>404Coin</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>True</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>1.055185e+09</td>\n",
       "      <td>532000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>SixEleven</td>\n",
       "      <td>SHA-256</td>\n",
       "      <td>True</td>\n",
       "      <td>PoW</td>\n",
       "      <td>NaN</td>\n",
       "      <td>611000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808</th>\n",
       "      <td>808</td>\n",
       "      <td>SHA-256</td>\n",
       "      <td>True</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>EliteCoin</td>\n",
       "      <td>X13</td>\n",
       "      <td>True</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>2.927942e+10</td>\n",
       "      <td>314159265359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>2015 coin</td>\n",
       "      <td>X11</td>\n",
       "      <td>True</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BTC</th>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>SHA-256</td>\n",
       "      <td>True</td>\n",
       "      <td>PoW</td>\n",
       "      <td>1.792718e+07</td>\n",
       "      <td>21000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ETH</th>\n",
       "      <td>Ethereum</td>\n",
       "      <td>Ethash</td>\n",
       "      <td>True</td>\n",
       "      <td>PoW</td>\n",
       "      <td>1.076842e+08</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LTC</th>\n",
       "      <td>Litecoin</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>True</td>\n",
       "      <td>PoW</td>\n",
       "      <td>6.303924e+07</td>\n",
       "      <td>84000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CoinName Algorithm  IsTrading ProofType  TotalCoinsMined  \\\n",
       "42      42 Coin    Scrypt       True   PoW/PoS     4.199995e+01   \n",
       "365     365Coin       X11       True   PoW/PoS              NaN   \n",
       "404     404Coin    Scrypt       True   PoW/PoS     1.055185e+09   \n",
       "611   SixEleven   SHA-256       True       PoW              NaN   \n",
       "808         808   SHA-256       True   PoW/PoS     0.000000e+00   \n",
       "1337  EliteCoin       X13       True   PoW/PoS     2.927942e+10   \n",
       "2015  2015 coin       X11       True   PoW/PoS              NaN   \n",
       "BTC     Bitcoin   SHA-256       True       PoW     1.792718e+07   \n",
       "ETH    Ethereum    Ethash       True       PoW     1.076842e+08   \n",
       "LTC    Litecoin    Scrypt       True       PoW     6.303924e+07   \n",
       "\n",
       "     TotalCoinSupply  \n",
       "42                42  \n",
       "365       2300000000  \n",
       "404        532000000  \n",
       "611           611000  \n",
       "808                0  \n",
       "1337    314159265359  \n",
       "2015               0  \n",
       "BTC         21000000  \n",
       "ETH                0  \n",
       "LTC         84000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import Dataset\n",
    "df = pd.read_csv(\"../data/crypto_data.csv\", index_col=0)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List the algorithms that are in use\n",
    "alg = set(df[\"Algorithm\"].values)\n",
    "type(alg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     1144\n",
       "False     108\n",
       "Name: IsTrading, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View amount of active crypto coins\n",
    "df[\"IsTrading\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True    1144\n",
       "Name: IsTrading, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter by the ones that are being actively traded\n",
    "active_df = df.loc[df[\"IsTrading\"] == True]\n",
    "active_df[\"IsTrading\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CoinName</th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>ProofType</th>\n",
       "      <th>TotalCoinsMined</th>\n",
       "      <th>TotalCoinSupply</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42 Coin</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>4.199995e+01</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>365Coin</td>\n",
       "      <td>X11</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2300000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>404Coin</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>1.055185e+09</td>\n",
       "      <td>532000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>SixEleven</td>\n",
       "      <td>SHA-256</td>\n",
       "      <td>PoW</td>\n",
       "      <td>NaN</td>\n",
       "      <td>611000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808</th>\n",
       "      <td>808</td>\n",
       "      <td>SHA-256</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CoinName Algorithm ProofType  TotalCoinsMined TotalCoinSupply\n",
       "42     42 Coin    Scrypt   PoW/PoS     4.199995e+01              42\n",
       "365    365Coin       X11   PoW/PoS              NaN      2300000000\n",
       "404    404Coin    Scrypt   PoW/PoS     1.055185e+09       532000000\n",
       "611  SixEleven   SHA-256       PoW              NaN          611000\n",
       "808        808   SHA-256   PoW/PoS     0.000000e+00               0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Delete the IsTrading column\n",
    "df = active_df.drop(\"IsTrading\", axis=1)\n",
    "# df = active_df[[\"CoinName\", \"Algorithm\", \"ProofType\", \"TotalCoinsMined\", \"TotalCoinSupply\"]]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>CoinName</th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>ProofType</th>\n",
       "      <th>TotalCoinsMined</th>\n",
       "      <th>TotalCoinSupply</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42 Coin</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>4.199995e+01</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>404Coin</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>1.055185e+09</td>\n",
       "      <td>532000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808</th>\n",
       "      <td>808</td>\n",
       "      <td>SHA-256</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>EliteCoin</td>\n",
       "      <td>X13</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>2.927942e+10</td>\n",
       "      <td>314159265359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BTC</th>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>SHA-256</td>\n",
       "      <td>PoW</td>\n",
       "      <td>1.792718e+07</td>\n",
       "      <td>21000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CoinName Algorithm ProofType  TotalCoinsMined TotalCoinSupply\n",
       "42      42 Coin    Scrypt   PoW/PoS     4.199995e+01              42\n",
       "404     404Coin    Scrypt   PoW/PoS     1.055185e+09       532000000\n",
       "808         808   SHA-256   PoW/PoS     0.000000e+00               0\n",
       "1337  EliteCoin       X13   PoW/PoS     2.927942e+10    314159265359\n",
       "BTC     Bitcoin   SHA-256       PoW     1.792718e+07        21000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop null value rows\n",
    "df = df.dropna(axis=0, how=\"any\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FIII   -5.917978e+09\n",
       "LBTC    0.000000e+00\n",
       "RIPO    0.000000e+00\n",
       "BASH    0.000000e+00\n",
       "CSH     0.000000e+00\n",
       "            ...     \n",
       "QWC     9.955311e+10\n",
       "NYC     1.430067e+11\n",
       "GCN     1.630551e+11\n",
       "BCN     1.840668e+11\n",
       "BTT     9.899887e+11\n",
       "Name: TotalCoinsMined, Length: 685, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort values to identify negative / zero data points\n",
    "df[\"TotalCoinsMined\"].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CoinName</th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>ProofType</th>\n",
       "      <th>TotalCoinsMined</th>\n",
       "      <th>TotalCoinSupply</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42 Coin</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>4.199995e+01</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>404Coin</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>1.055185e+09</td>\n",
       "      <td>532000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>EliteCoin</td>\n",
       "      <td>X13</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>2.927942e+10</td>\n",
       "      <td>314159265359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BTC</th>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>SHA-256</td>\n",
       "      <td>PoW</td>\n",
       "      <td>1.792718e+07</td>\n",
       "      <td>21000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ETH</th>\n",
       "      <td>Ethereum</td>\n",
       "      <td>Ethash</td>\n",
       "      <td>PoW</td>\n",
       "      <td>1.076842e+08</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CoinName Algorithm ProofType  TotalCoinsMined TotalCoinSupply\n",
       "42      42 Coin    Scrypt   PoW/PoS     4.199995e+01              42\n",
       "404     404Coin    Scrypt   PoW/PoS     1.055185e+09       532000000\n",
       "1337  EliteCoin       X13   PoW/PoS     2.927942e+10    314159265359\n",
       "BTC     Bitcoin   SHA-256       PoW     1.792718e+07        21000000\n",
       "ETH    Ethereum    Ethash       PoW     1.076842e+08               0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean out those nagative / zero data points\n",
    "df = df.loc[df[\"TotalCoinsMined\"] > 0]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "532"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the new length of cleaned dataset\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Algorithm</th>\n",
       "      <th>ProofType</th>\n",
       "      <th>TotalCoinsMined</th>\n",
       "      <th>TotalCoinSupply</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Scrypt</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>4.199995e+01</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>Scrypt</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>1.055185e+09</td>\n",
       "      <td>532000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>X13</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>2.927942e+10</td>\n",
       "      <td>314159265359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BTC</th>\n",
       "      <td>SHA-256</td>\n",
       "      <td>PoW</td>\n",
       "      <td>1.792718e+07</td>\n",
       "      <td>21000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ETH</th>\n",
       "      <td>Ethash</td>\n",
       "      <td>PoW</td>\n",
       "      <td>1.076842e+08</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Algorithm ProofType  TotalCoinsMined TotalCoinSupply\n",
       "42      Scrypt   PoW/PoS     4.199995e+01              42\n",
       "404     Scrypt   PoW/PoS     1.055185e+09       532000000\n",
       "1337       X13   PoW/PoS     2.927942e+10    314159265359\n",
       "BTC    SHA-256       PoW     1.792718e+07        21000000\n",
       "ETH     Ethash       PoW     1.076842e+08               0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(\"CoinName\", axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(532, 98)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TotalCoinsMined</th>\n",
       "      <th>TotalCoinSupply</th>\n",
       "      <th>Algorithm_1GB AES Pattern Search</th>\n",
       "      <th>Algorithm_536</th>\n",
       "      <th>Algorithm_Argon2d</th>\n",
       "      <th>Algorithm_BLAKE256</th>\n",
       "      <th>Algorithm_Blake</th>\n",
       "      <th>Algorithm_Blake2S</th>\n",
       "      <th>Algorithm_Blake2b</th>\n",
       "      <th>Algorithm_C11</th>\n",
       "      <th>...</th>\n",
       "      <th>ProofType_PoW/PoS</th>\n",
       "      <th>ProofType_PoW/PoS</th>\n",
       "      <th>ProofType_PoW/PoW</th>\n",
       "      <th>ProofType_PoW/nPoS</th>\n",
       "      <th>ProofType_Pos</th>\n",
       "      <th>ProofType_Proof of Authority</th>\n",
       "      <th>ProofType_Proof of Trust</th>\n",
       "      <th>ProofType_TPoS</th>\n",
       "      <th>ProofType_Zero-Knowledge Proof</th>\n",
       "      <th>ProofType_dPoW/PoW</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4.199995e+01</td>\n",
       "      <td>42</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>1.055185e+09</td>\n",
       "      <td>532000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>2.927942e+10</td>\n",
       "      <td>314159265359</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BTC</th>\n",
       "      <td>1.792718e+07</td>\n",
       "      <td>21000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ETH</th>\n",
       "      <td>1.076842e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 98 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      TotalCoinsMined TotalCoinSupply  Algorithm_1GB AES Pattern Search  \\\n",
       "42       4.199995e+01              42                                 0   \n",
       "404      1.055185e+09       532000000                                 0   \n",
       "1337     2.927942e+10    314159265359                                 0   \n",
       "BTC      1.792718e+07        21000000                                 0   \n",
       "ETH      1.076842e+08               0                                 0   \n",
       "\n",
       "      Algorithm_536  Algorithm_Argon2d  Algorithm_BLAKE256  Algorithm_Blake  \\\n",
       "42                0                  0                   0                0   \n",
       "404               0                  0                   0                0   \n",
       "1337              0                  0                   0                0   \n",
       "BTC               0                  0                   0                0   \n",
       "ETH               0                  0                   0                0   \n",
       "\n",
       "      Algorithm_Blake2S  Algorithm_Blake2b  Algorithm_C11  ...  \\\n",
       "42                    0                  0              0  ...   \n",
       "404                   0                  0              0  ...   \n",
       "1337                  0                  0              0  ...   \n",
       "BTC                   0                  0              0  ...   \n",
       "ETH                   0                  0              0  ...   \n",
       "\n",
       "      ProofType_PoW/PoS  ProofType_PoW/PoS   ProofType_PoW/PoW  \\\n",
       "42                    1                   0                  0   \n",
       "404                   1                   0                  0   \n",
       "1337                  1                   0                  0   \n",
       "BTC                   0                   0                  0   \n",
       "ETH                   0                   0                  0   \n",
       "\n",
       "      ProofType_PoW/nPoS  ProofType_Pos  ProofType_Proof of Authority  \\\n",
       "42                     0              0                             0   \n",
       "404                    0              0                             0   \n",
       "1337                   0              0                             0   \n",
       "BTC                    0              0                             0   \n",
       "ETH                    0              0                             0   \n",
       "\n",
       "      ProofType_Proof of Trust  ProofType_TPoS  \\\n",
       "42                           0               0   \n",
       "404                          0               0   \n",
       "1337                         0               0   \n",
       "BTC                          0               0   \n",
       "ETH                          0               0   \n",
       "\n",
       "      ProofType_Zero-Knowledge Proof  ProofType_dPoW/PoW  \n",
       "42                                 0                   0  \n",
       "404                                0                   0  \n",
       "1337                               0                   0  \n",
       "BTC                                0                   0  \n",
       "ETH                                0                   0  \n",
       "\n",
       "[5 rows x 98 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create features list & target for Unsupervised ML Model\n",
    "X = pd.get_dummies(data=df, columns=[\"Algorithm\", \"ProofType\"])\n",
    "print(X.shape)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scaling the features list dataset\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(532, 98)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the size of the dataframe to ensure it is the proper size\n",
    "X_scaled.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reducing dataset dimension with PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(532, 74)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reduce dimension with PCA \n",
    "pca = PCA(n_components=.90)\n",
    "principal_compnenets = pca.fit_transform(X_scaled)\n",
    "principal_compnenets.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "88.47283457566155\n"
     ]
    }
   ],
   "source": [
    "# Variance levels display of the princiapl_components line above\n",
    "print(pca.explained_variance_.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(532, 2)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reduce dimensions with t-SNE\n",
    "tsne = TSNE(perplexity=50)\n",
    "tsne_feature = tsne.fit_transform(principal_compnenets)\n",
    "tsne_feature.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot T-SNE features\n",
    "X = tsne_feature[:,0]\n",
    "y = tsne_feature[:,1]\n",
    "\n",
    "# Display plt\n",
    "plt.scatter(X,y)\n",
    "plt.title(\"T-SNE Feature Clusters\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Identify cluster with k-means\n",
    "inertia = []\n",
    "k = list(range(1,11))\n",
    "\n",
    "for i in k:\n",
    "    kmeans = KMeans(n_clusters=i, random_state=0)\n",
    "    kmeans.fit(principal_compnenets)\n",
    "    inertia.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Elbow plot\n",
    "elbow_df = pd.DataFrame({\"k\": k, \"inertia\": inertia})\n",
    "elbow_df.plot.line(x=\"k\", y=\"inertia\")\n",
    "plt.xlabel(\"K Means\")\n",
    "plt.ylabel(\"Inertia\")\n",
    "plt.title(\"Elbow Plot\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "The T-SNE feature plut and the K-Means elbow plot suggests that there is 'potentially' a meaningful cluster in this dataset. However, I will conclude by saying there is not enough here to support meaningful clistering, regardless of potential value. Our elbow chart trends downwards with no elbow point to suggest a meaningful selection value. An optimal point for K-Mean selection using the elbow plot would be 6. However, this wouldn't yield good results as the elbow persists downwards. In short, there is not enough of a 'elbow' in our K-Means plot to signify a meaningful cluster in this dataset. Although 6 'maybe' used, it is not recommended based on what is seen here."
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "920830383a62d6fe7767526598dd61951e1b354c578ca4e90929169c56629948"
  },
  "kernelspec": {
   "display_name": "Python 3.8.8 64-bit ('base': conda)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
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}