crypto_clusters_hw.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#GO ETHEREUM!!\n",
    "#To the Moon!!\n",
    "\n",
    "#imports\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>CoinName</th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>IsTrading</th>\n",
       "      <th>ProofType</th>\n",
       "      <th>TotalCoinsMined</th>\n",
       "      <th>TotalCoinSupply</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42</td>\n",
       "      <td>42 Coin</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>True</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>4.199995e+01</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>365</td>\n",
       "      <td>365Coin</td>\n",
       "      <td>X11</td>\n",
       "      <td>True</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2300000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>404</td>\n",
       "      <td>404Coin</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>True</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>1.055185e+09</td>\n",
       "      <td>532000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>611</td>\n",
       "      <td>SixEleven</td>\n",
       "      <td>SHA-256</td>\n",
       "      <td>True</td>\n",
       "      <td>PoW</td>\n",
       "      <td>NaN</td>\n",
       "      <td>611000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>808</td>\n",
       "      <td>808</td>\n",
       "      <td>SHA-256</td>\n",
       "      <td>True</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1337</td>\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>6</th>\n",
       "      <td>2015</td>\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>7</th>\n",
       "      <td>BTC</td>\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>8</th>\n",
       "      <td>ETH</td>\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>9</th>\n",
       "      <td>LTC</td>\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": [
       "  Unnamed: 0   CoinName Algorithm  IsTrading ProofType  TotalCoinsMined  \\\n",
       "0         42    42 Coin    Scrypt       True   PoW/PoS     4.199995e+01   \n",
       "1        365    365Coin       X11       True   PoW/PoS              NaN   \n",
       "2        404    404Coin    Scrypt       True   PoW/PoS     1.055185e+09   \n",
       "3        611  SixEleven   SHA-256       True       PoW              NaN   \n",
       "4        808        808   SHA-256       True   PoW/PoS     0.000000e+00   \n",
       "5       1337  EliteCoin       X13       True   PoW/PoS     2.927942e+10   \n",
       "6       2015  2015 coin       X11       True   PoW/PoS              NaN   \n",
       "7        BTC    Bitcoin   SHA-256       True       PoW     1.792718e+07   \n",
       "8        ETH   Ethereum    Ethash       True       PoW     1.076842e+08   \n",
       "9        LTC   Litecoin    Scrypt       True       PoW     6.303924e+07   \n",
       "\n",
       "  TotalCoinSupply  \n",
       "0              42  \n",
       "1      2300000000  \n",
       "2       532000000  \n",
       "3          611000  \n",
       "4               0  \n",
       "5    314159265359  \n",
       "6               0  \n",
       "7        21000000  \n",
       "8               0  \n",
       "9        84000000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read file path in\n",
    "\n",
    "crypto_raw_df = pd.read_csv(Path('Resources/crypto_data.csv'))\n",
    "crypto_raw_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>0</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>1</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>2</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>3</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>4</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>5</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>6</th>\n",
       "      <td>2015 coin</td>\n",
       "      <td>X11</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</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>8</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",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Litecoin</td>\n",
       "      <td>Scrypt</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 ProofType  TotalCoinsMined TotalCoinSupply\n",
       "0    42 Coin    Scrypt   PoW/PoS     4.199995e+01              42\n",
       "1    365Coin       X11   PoW/PoS              NaN      2300000000\n",
       "2    404Coin    Scrypt   PoW/PoS     1.055185e+09       532000000\n",
       "3  SixEleven   SHA-256       PoW              NaN          611000\n",
       "4        808   SHA-256   PoW/PoS     0.000000e+00               0\n",
       "5  EliteCoin       X13   PoW/PoS     2.927942e+10    314159265359\n",
       "6  2015 coin       X11   PoW/PoS              NaN               0\n",
       "7    Bitcoin   SHA-256       PoW     1.792718e+07        21000000\n",
       "8   Ethereum    Ethash       PoW     1.076842e+08               0\n",
       "9   Litecoin    Scrypt       PoW     6.303924e+07        84000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# clean traded data\n",
    "\n",
    "cleaned_crypto_df = crypto_raw_df.loc[crypto_raw_df[\"IsTrading\"]==True]\n",
    "cleaned_crypto_df = cleaned_crypto_df.drop(columns=['Unnamed: 0','IsTrading'],axis=1)\n",
    "cleaned_crypto_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>0</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>2</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>4</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>5</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>7</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>8</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",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Litecoin</td>\n",
       "      <td>Scrypt</td>\n",
       "      <td>PoW</td>\n",
       "      <td>6.303924e+07</td>\n",
       "      <td>84000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Dash</td>\n",
       "      <td>X11</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>9.031294e+06</td>\n",
       "      <td>22000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Monero</td>\n",
       "      <td>CryptoNight-V7</td>\n",
       "      <td>PoW</td>\n",
       "      <td>1.720114e+07</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ethereum Classic</td>\n",
       "      <td>Ethash</td>\n",
       "      <td>PoW</td>\n",
       "      <td>1.133597e+08</td>\n",
       "      <td>210000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CoinName       Algorithm ProofType  TotalCoinsMined  \\\n",
       "0            42 Coin          Scrypt   PoW/PoS     4.199995e+01   \n",
       "2            404Coin          Scrypt   PoW/PoS     1.055185e+09   \n",
       "4                808         SHA-256   PoW/PoS     0.000000e+00   \n",
       "5          EliteCoin             X13   PoW/PoS     2.927942e+10   \n",
       "7            Bitcoin         SHA-256       PoW     1.792718e+07   \n",
       "8           Ethereum          Ethash       PoW     1.076842e+08   \n",
       "9           Litecoin          Scrypt       PoW     6.303924e+07   \n",
       "10              Dash             X11   PoW/PoS     9.031294e+06   \n",
       "11            Monero  CryptoNight-V7       PoW     1.720114e+07   \n",
       "12  Ethereum Classic          Ethash       PoW     1.133597e+08   \n",
       "\n",
       "   TotalCoinSupply  \n",
       "0               42  \n",
       "2        532000000  \n",
       "4                0  \n",
       "5     314159265359  \n",
       "7         21000000  \n",
       "8                0  \n",
       "9         84000000  \n",
       "10        22000000  \n",
       "11               0  \n",
       "12       210000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove data which houses null \n",
    "\n",
    "cleaned_crypto_df = cleaned_crypto_df.dropna(axis=0)\n",
    "cleaned_crypto_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>0</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>2</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>5</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>7</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>8</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",
       "0    42 Coin    Scrypt   PoW/PoS     4.199995e+01              42\n",
       "2    404Coin    Scrypt   PoW/PoS     1.055185e+09       532000000\n",
       "5  EliteCoin       X13   PoW/PoS     2.927942e+10    314159265359\n",
       "7    Bitcoin   SHA-256       PoW     1.792718e+07        21000000\n",
       "8   Ethereum    Ethash       PoW     1.076842e+08               0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# filter by mined currency greater than zero\n",
    "mined_df = cleaned_crypto_df.loc[cleaned_crypto_df.TotalCoinsMined > 0]\n",
    "mined_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>Algorithm</th>\n",
       "      <th>ProofType</th>\n",
       "      <th>TotalCoinsMined</th>\n",
       "      <th>TotalCoinSupply</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Scrypt</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>4.199995e+01</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Scrypt</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>1.055185e+09</td>\n",
       "      <td>532000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>X13</td>\n",
       "      <td>PoW/PoS</td>\n",
       "      <td>2.927942e+10</td>\n",
       "      <td>314159265359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SHA-256</td>\n",
       "      <td>PoW</td>\n",
       "      <td>1.792718e+07</td>\n",
       "      <td>21000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>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",
       "0    Scrypt   PoW/PoS     4.199995e+01              42\n",
       "2    Scrypt   PoW/PoS     1.055185e+09       532000000\n",
       "5       X13   PoW/PoS     2.927942e+10    314159265359\n",
       "7   SHA-256       PoW     1.792718e+07        21000000\n",
       "8    Ethash       PoW     1.076842e+08               0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop coin name\n",
    "mined_df = mined_df.drop(columns='CoinName',axis=1)\n",
    "mined_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#grab length add in dummy df\n",
    "dummy_df = pd.get_dummies(mined_df, columns=['Algorithm', 'ProofType'])\n",
    "len(dummy_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#standardize dataset\n",
    "scaler = StandardScaler()\n",
    "scaled_dummy_df = scaler.fit_transform(dummy_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PCA1</th>\n",
       "      <th>PCA2</th>\n",
       "      <th>PCA3</th>\n",
       "      <th>PCA4</th>\n",
       "      <th>PCA5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.347984</td>\n",
       "      <td>0.988608</td>\n",
       "      <td>-0.578635</td>\n",
       "      <td>-0.003495</td>\n",
       "      <td>0.012994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.331292</td>\n",
       "      <td>0.988973</td>\n",
       "      <td>-0.578967</td>\n",
       "      <td>-0.003599</td>\n",
       "      <td>0.012946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.313127</td>\n",
       "      <td>1.624978</td>\n",
       "      <td>-0.712697</td>\n",
       "      <td>-0.012486</td>\n",
       "      <td>0.021749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.134522</td>\n",
       "      <td>-1.306795</td>\n",
       "      <td>0.192720</td>\n",
       "      <td>0.003897</td>\n",
       "      <td>-0.009418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.144353</td>\n",
       "      <td>-2.012178</td>\n",
       "      <td>0.394858</td>\n",
       "      <td>-0.003089</td>\n",
       "      <td>0.019737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PCA1      PCA2      PCA3      PCA4      PCA5\n",
       "0 -0.347984  0.988608 -0.578635 -0.003495  0.012994\n",
       "1 -0.331292  0.988973 -0.578967 -0.003599  0.012946\n",
       "2  2.313127  1.624978 -0.712697 -0.012486  0.021749\n",
       "3 -0.134522 -1.306795  0.192720  0.003897 -0.009418\n",
       "4 -0.144353 -2.012178  0.394858 -0.003089  0.019737"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#establish PCA model; grab variances\n",
    "pca=PCA(n_components = 5)\n",
    "\n",
    "pca_df=pca.fit_transform(scaled_dummy_df)\n",
    "\n",
    "pca_df_coin = pd.DataFrame(data=pca_df, columns=[\"PCA1\", \"PCA2\", \"PCA3\", \"PCA4\", \"PCA5\"])\n",
    "pca_df_coin.head(5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02793077, 0.02139217, 0.02051093, 0.02044481, 0.02044031])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#format/dispalay array\n",
    "\n",
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-3.35098808e-01,  1.03218907e+00, -5.90713438e-01, ...,\n",
       "         7.87306563e-16, -1.84616729e-18, -4.15361072e-15],\n",
       "       [-3.18433948e-01,  1.03233143e+00, -5.91125551e-01, ...,\n",
       "         1.17409449e-15,  5.25116470e-16, -3.80497340e-15],\n",
       "       [ 2.30546811e+00,  1.65638302e+00, -6.83616670e-01, ...,\n",
       "         2.75207903e-14, -2.36464424e-14,  2.84400186e-14],\n",
       "       ...,\n",
       "       [ 3.24348535e-01, -2.31230764e+00,  4.21515366e-01, ...,\n",
       "         1.93578889e-14, -8.67896697e-15, -3.66917605e-14],\n",
       "       [-1.49363652e-01, -2.04933553e+00,  4.12968257e-01, ...,\n",
       "        -1.78255127e-15, -2.03579310e-16, -4.38160754e-16],\n",
       "       [-2.89957500e-01,  8.21194511e-01, -2.76326847e-01, ...,\n",
       "        -4.70588149e-16, -9.07306180e-17, -2.78763670e-15]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#transformed variance array\n",
    "pca_clean = PCA(n_components=.90)\n",
    "crypto_pca_clean = pca_clean.fit_transform(scaled_dummy_df)\n",
    "\n",
    "crypto_pca_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "      <th>5</th>\n",
       "      <th>6</th>\n",
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       "      <th>8</th>\n",
       "      <th>9</th>\n",
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       "      <th>64</th>\n",
       "      <th>65</th>\n",
       "      <th>66</th>\n",
       "      <th>67</th>\n",
       "      <th>68</th>\n",
       "      <th>69</th>\n",
       "      <th>70</th>\n",
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       "      <td>-0.007129</td>\n",
       "      <td>-1.319756</td>\n",
       "      <td>-0.051813</td>\n",
       "      <td>...</td>\n",
       "      <td>7.930746e-17</td>\n",
       "      <td>1.066184e-15</td>\n",
       "      <td>2.929092e-15</td>\n",
       "      <td>-7.260564e-16</td>\n",
       "      <td>-1.958946e-15</td>\n",
       "      <td>1.858141e-15</td>\n",
       "      <td>-1.863509e-15</td>\n",
       "      <td>7.873066e-16</td>\n",
       "      <td>-1.846167e-18</td>\n",
       "      <td>-4.153611e-15</td>\n",
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       "      <th>1</th>\n",
       "      <td>-0.318434</td>\n",
       "      <td>1.032331</td>\n",
       "      <td>-0.591126</td>\n",
       "      <td>0.001386</td>\n",
       "      <td>2.712386e-12</td>\n",
       "      <td>-5.575109e-15</td>\n",
       "      <td>-2.141355e-16</td>\n",
       "      <td>-0.007739</td>\n",
       "      <td>-1.322514</td>\n",
       "      <td>-0.056674</td>\n",
       "      <td>...</td>\n",
       "      <td>5.236227e-17</td>\n",
       "      <td>3.558103e-16</td>\n",
       "      <td>3.123007e-15</td>\n",
       "      <td>-5.243003e-17</td>\n",
       "      <td>-1.703046e-15</td>\n",
       "      <td>1.069014e-15</td>\n",
       "      <td>-1.856951e-15</td>\n",
       "      <td>1.174094e-15</td>\n",
       "      <td>5.251165e-16</td>\n",
       "      <td>-3.804973e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.305468</td>\n",
       "      <td>1.656383</td>\n",
       "      <td>-0.683617</td>\n",
       "      <td>0.004731</td>\n",
       "      <td>9.322259e-12</td>\n",
       "      <td>-6.700325e-15</td>\n",
       "      <td>7.872127e-15</td>\n",
       "      <td>-0.054781</td>\n",
       "      <td>-1.542879</td>\n",
       "      <td>-0.943951</td>\n",
       "      <td>...</td>\n",
       "      <td>3.638111e-14</td>\n",
       "      <td>-1.841986e-14</td>\n",
       "      <td>-5.521324e-14</td>\n",
       "      <td>-7.303804e-14</td>\n",
       "      <td>2.329683e-14</td>\n",
       "      <td>-3.527460e-14</td>\n",
       "      <td>3.114482e-14</td>\n",
       "      <td>2.752079e-14</td>\n",
       "      <td>-2.364644e-14</td>\n",
       "      <td>2.844002e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.145184</td>\n",
       "      <td>-1.320593</td>\n",
       "      <td>0.192813</td>\n",
       "      <td>-0.001229</td>\n",
       "      <td>-2.420624e-12</td>\n",
       "      <td>2.357806e-15</td>\n",
       "      <td>-1.697979e-15</td>\n",
       "      <td>-0.002071</td>\n",
       "      <td>0.281463</td>\n",
       "      <td>0.251862</td>\n",
       "      <td>...</td>\n",
       "      <td>4.765898e-15</td>\n",
       "      <td>2.352318e-17</td>\n",
       "      <td>-2.522688e-14</td>\n",
       "      <td>6.604406e-15</td>\n",
       "      <td>-9.542520e-15</td>\n",
       "      <td>-1.391009e-14</td>\n",
       "      <td>2.591484e-15</td>\n",
       "      <td>2.225494e-15</td>\n",
       "      <td>4.614234e-15</td>\n",
       "      <td>4.660314e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.151768</td>\n",
       "      <td>-2.036192</td>\n",
       "      <td>0.396182</td>\n",
       "      <td>-0.001705</td>\n",
       "      <td>-3.352037e-12</td>\n",
       "      <td>2.706411e-15</td>\n",
       "      <td>-1.587523e-15</td>\n",
       "      <td>0.027735</td>\n",
       "      <td>0.519099</td>\n",
       "      <td>0.188820</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.035501e-14</td>\n",
       "      <td>6.137487e-14</td>\n",
       "      <td>-3.172419e-14</td>\n",
       "      <td>-3.873903e-14</td>\n",
       "      <td>-7.522860e-14</td>\n",
       "      <td>-7.057959e-14</td>\n",
       "      <td>4.784940e-14</td>\n",
       "      <td>9.436182e-15</td>\n",
       "      <td>1.251016e-13</td>\n",
       "      <td>-3.544121e-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2         3             4             5   \\\n",
       "0 -0.335099  1.032189 -0.590713  0.001397  2.734816e-12 -5.417255e-15   \n",
       "1 -0.318434  1.032331 -0.591126  0.001386  2.712386e-12 -5.575109e-15   \n",
       "2  2.305468  1.656383 -0.683617  0.004731  9.322259e-12 -6.700325e-15   \n",
       "3 -0.145184 -1.320593  0.192813 -0.001229 -2.420624e-12  2.357806e-15   \n",
       "4 -0.151768 -2.036192  0.396182 -0.001705 -3.352037e-12  2.706411e-15   \n",
       "\n",
       "             6         7         8         9   ...            64  \\\n",
       "0 -1.223976e-16 -0.007129 -1.319756 -0.051813  ...  7.930746e-17   \n",
       "1 -2.141355e-16 -0.007739 -1.322514 -0.056674  ...  5.236227e-17   \n",
       "2  7.872127e-15 -0.054781 -1.542879 -0.943951  ...  3.638111e-14   \n",
       "3 -1.697979e-15 -0.002071  0.281463  0.251862  ...  4.765898e-15   \n",
       "4 -1.587523e-15  0.027735  0.519099  0.188820  ... -3.035501e-14   \n",
       "\n",
       "             65            66            67            68            69  \\\n",
       "0  1.066184e-15  2.929092e-15 -7.260564e-16 -1.958946e-15  1.858141e-15   \n",
       "1  3.558103e-16  3.123007e-15 -5.243003e-17 -1.703046e-15  1.069014e-15   \n",
       "2 -1.841986e-14 -5.521324e-14 -7.303804e-14  2.329683e-14 -3.527460e-14   \n",
       "3  2.352318e-17 -2.522688e-14  6.604406e-15 -9.542520e-15 -1.391009e-14   \n",
       "4  6.137487e-14 -3.172419e-14 -3.873903e-14 -7.522860e-14 -7.057959e-14   \n",
       "\n",
       "             70            71            72            73  \n",
       "0 -1.863509e-15  7.873066e-16 -1.846167e-18 -4.153611e-15  \n",
       "1 -1.856951e-15  1.174094e-15  5.251165e-16 -3.804973e-15  \n",
       "2  3.114482e-14  2.752079e-14 -2.364644e-14  2.844002e-14  \n",
       "3  2.591484e-15  2.225494e-15  4.614234e-15  4.660314e-15  \n",
       "4  4.784940e-14  9.436182e-15  1.251016e-13 -3.544121e-15  \n",
       "\n",
       "[5 rows x 74 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#transform Principal Component Analysis to DataFrame\n",
    "transformed_df = pd.DataFrame(data=crypto_pca_clean)\n",
    "transformed_df.head(5)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x12a815bb0>,\n",
       "  <matplotlib.axis.XTick at 0x12a815b80>,\n",
       "  <matplotlib.axis.XTick at 0x12a8126a0>,\n",
       "  <matplotlib.axis.XTick at 0x12a8b5130>,\n",
       "  <matplotlib.axis.XTick at 0x12a8b5640>],\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting variances v features\n",
    "features = range(pca.n_components_)\n",
    "plt.bar(features, pca.explained_variance_ratio_)\n",
    "plt.xlabel('PCA Features')\n",
    "plt.ylabel('Percent Variance')\n",
    "plt.xticks(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TSNE(learning_rate=50)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#establish learning rate\n",
    "tsne = TSNE(learning_rate=50)\n",
    "tsne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(532, 2)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#generate tsne output\n",
    "tsne_features = tsne.fit_transform(transformed_df)\n",
    "tsne_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#establish matplot graph for tsne generated info\n",
    "\n",
    "x = tsne_features[:,0]\n",
    "y = tsne_features[:,1]\n",
    "plt.scatter(x, y)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#generate elbow graph w/ kmeans\n",
    "\n",
    "inertia_container = []\n",
    "l_range = list(range(1, 11))\n",
    "\n",
    "for i in l_range:\n",
    "    km = KMeans(n_clusters=i, random_state=42)\n",
    "    km.fit(transformed_df)\n",
    "    inertia_container.append(km.inertia_)\n",
    "\n",
    "# Elbow Curve\n",
    "elbow_data = {\"K\": l_range, \"Inertia\": inertia_container}\n",
    "df_elbow = pd.DataFrame(elbow_data)\n",
    "plt.plot(df_elbow['K'], df_elbow['Inertia'])\n",
    "plt.xticks(range(1,11))\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('Generated Inertia')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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