PCA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import Normalizer, StandardScaler, MinMaxScaler, RobustScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_absolute_error, r2_score, mean_squared_error\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('BTC_Data_736_features_raw.csv',sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.interpolate(axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "interval3=(data['Date'] >= '2013/04/01') & (data['Date'] <= '2019/12/31')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=data.loc[interval3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>priceUSD</th>\n",
       "      <th>transactions</th>\n",
       "      <th>size</th>\n",
       "      <th>sentbyaddress</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>hashrate</th>\n",
       "      <th>mining_profitability</th>\n",
       "      <th>sentinusdUSD</th>\n",
       "      <th>transactionfeesUSD</th>\n",
       "      <th>...</th>\n",
       "      <th>price3rsiUSD</th>\n",
       "      <th>price7rsiUSD</th>\n",
       "      <th>price14rsiUSD</th>\n",
       "      <th>price30rsiUSD</th>\n",
       "      <th>price90rsiUSD</th>\n",
       "      <th>price3rocUSD</th>\n",
       "      <th>price7rocUSD</th>\n",
       "      <th>price14rocUSD</th>\n",
       "      <th>price30rocUSD</th>\n",
       "      <th>price90rocUSD</th>\n",
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       "  </thead>\n",
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       "      <th>989</th>\n",
       "      <td>2013/04/01</td>\n",
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       "      <td>0.0777</td>\n",
       "      <td>...</td>\n",
       "      <td>99.456</td>\n",
       "      <td>96.992</td>\n",
       "      <td>95.075</td>\n",
       "      <td>93.774</td>\n",
       "      <td>91.450</td>\n",
       "      <td>9.383</td>\n",
       "      <td>36.980</td>\n",
       "      <td>104.747</td>\n",
       "      <td>185.993</td>\n",
       "      <td>606.456</td>\n",
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       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>2013/04/02</td>\n",
       "      <td>107.749</td>\n",
       "      <td>63095</td>\n",
       "      <td>175443.0</td>\n",
       "      <td>62276</td>\n",
       "      <td>6695826.0</td>\n",
       "      <td>6.184560e+13</td>\n",
       "      <td>7990.0</td>\n",
       "      <td>278422775.0</td>\n",
       "      <td>0.1050</td>\n",
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       "      <td>99.811</td>\n",
       "      <td>98.201</td>\n",
       "      <td>96.416</td>\n",
       "      <td>94.987</td>\n",
       "      <td>92.645</td>\n",
       "      <td>23.206</td>\n",
       "      <td>50.270</td>\n",
       "      <td>109.704</td>\n",
       "      <td>231.964</td>\n",
       "      <td>716.218</td>\n",
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       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>2013/04/03</td>\n",
       "      <td>120.003</td>\n",
       "      <td>63766</td>\n",
       "      <td>184209.0</td>\n",
       "      <td>69174</td>\n",
       "      <td>6695826.0</td>\n",
       "      <td>6.027037e+13</td>\n",
       "      <td>8852.0</td>\n",
       "      <td>284039909.0</td>\n",
       "      <td>0.1330</td>\n",
       "      <td>...</td>\n",
       "      <td>99.902</td>\n",
       "      <td>98.752</td>\n",
       "      <td>97.190</td>\n",
       "      <td>95.785</td>\n",
       "      <td>93.508</td>\n",
       "      <td>34.759</td>\n",
       "      <td>51.448</td>\n",
       "      <td>107.789</td>\n",
       "      <td>259.795</td>\n",
       "      <td>808.284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>2013/04/04</td>\n",
       "      <td>123.562</td>\n",
       "      <td>66738</td>\n",
       "      <td>221568.0</td>\n",
       "      <td>71753</td>\n",
       "      <td>6695826.0</td>\n",
       "      <td>5.527246e+13</td>\n",
       "      <td>8777.0</td>\n",
       "      <td>282809750.0</td>\n",
       "      <td>0.1410</td>\n",
       "      <td>...</td>\n",
       "      <td>99.919</td>\n",
       "      <td>98.869</td>\n",
       "      <td>97.368</td>\n",
       "      <td>95.977</td>\n",
       "      <td>93.724</td>\n",
       "      <td>30.456</td>\n",
       "      <td>44.093</td>\n",
       "      <td>93.098</td>\n",
       "      <td>237.355</td>\n",
       "      <td>834.762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>2013/04/05</td>\n",
       "      <td>128.664</td>\n",
       "      <td>61215</td>\n",
       "      <td>190067.0</td>\n",
       "      <td>69310</td>\n",
       "      <td>7005058.0</td>\n",
       "      <td>5.181545e+13</td>\n",
       "      <td>9950.0</td>\n",
       "      <td>264855827.0</td>\n",
       "      <td>0.1200</td>\n",
       "      <td>...</td>\n",
       "      <td>99.941</td>\n",
       "      <td>99.022</td>\n",
       "      <td>97.602</td>\n",
       "      <td>96.233</td>\n",
       "      <td>94.013</td>\n",
       "      <td>19.411</td>\n",
       "      <td>48.589</td>\n",
       "      <td>95.718</td>\n",
       "      <td>229.992</td>\n",
       "      <td>876.762</td>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 737 columns</p>\n",
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      "text/plain": [
       "           Date  priceUSD  transactions      size  sentbyaddress  difficulty  \\\n",
       "989  2013/04/01    94.715         52572  139256.0          48809   6695826.0   \n",
       "990  2013/04/02   107.749         63095  175443.0          62276   6695826.0   \n",
       "991  2013/04/03   120.003         63766  184209.0          69174   6695826.0   \n",
       "992  2013/04/04   123.562         66738  221568.0          71753   6695826.0   \n",
       "993  2013/04/05   128.664         61215  190067.0          69310   7005058.0   \n",
       "\n",
       "         hashrate  mining_profitability  sentinusdUSD  transactionfeesUSD  \\\n",
       "989  6.550211e+13                7220.0   136779480.0              0.0777   \n",
       "990  6.184560e+13                7990.0   278422775.0              0.1050   \n",
       "991  6.027037e+13                8852.0   284039909.0              0.1330   \n",
       "992  5.527246e+13                8777.0   282809750.0              0.1410   \n",
       "993  5.181545e+13                9950.0   264855827.0              0.1200   \n",
       "\n",
       "     ...  price3rsiUSD  price7rsiUSD  price14rsiUSD  price30rsiUSD  \\\n",
       "989  ...        99.456        96.992         95.075         93.774   \n",
       "990  ...        99.811        98.201         96.416         94.987   \n",
       "991  ...        99.902        98.752         97.190         95.785   \n",
       "992  ...        99.919        98.869         97.368         95.977   \n",
       "993  ...        99.941        99.022         97.602         96.233   \n",
       "\n",
       "     price90rsiUSD  price3rocUSD  price7rocUSD  price14rocUSD  price30rocUSD  \\\n",
       "989         91.450         9.383        36.980        104.747        185.993   \n",
       "990         92.645        23.206        50.270        109.704        231.964   \n",
       "991         93.508        34.759        51.448        107.789        259.795   \n",
       "992         93.724        30.456        44.093         93.098        237.355   \n",
       "993         94.013        19.411        48.589         95.718        229.992   \n",
       "\n",
       "     price90rocUSD  \n",
       "989        606.456  \n",
       "990        716.218  \n",
       "991        808.284  \n",
       "992        834.762  \n",
       "993        876.762  \n",
       "\n",
       "[5 rows x 737 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.iloc[:,2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <td>6695826.0</td>\n",
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       "      <td>8852.0</td>\n",
       "      <td>284039909.0</td>\n",
       "      <td>0.1330</td>\n",
       "      <td>0.0600</td>\n",
       "      <td>8.229</td>\n",
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       "      <td>99.902</td>\n",
       "      <td>98.752</td>\n",
       "      <td>97.190</td>\n",
       "      <td>95.785</td>\n",
       "      <td>93.508</td>\n",
       "      <td>34.759</td>\n",
       "      <td>51.448</td>\n",
       "      <td>107.789</td>\n",
       "      <td>259.795</td>\n",
       "      <td>808.284</td>\n",
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       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>66738</td>\n",
       "      <td>221568.0</td>\n",
       "      <td>71753</td>\n",
       "      <td>6695826.0</td>\n",
       "      <td>5.527246e+13</td>\n",
       "      <td>8777.0</td>\n",
       "      <td>282809750.0</td>\n",
       "      <td>0.1410</td>\n",
       "      <td>0.0618</td>\n",
       "      <td>9.351</td>\n",
       "      <td>...</td>\n",
       "      <td>99.919</td>\n",
       "      <td>98.869</td>\n",
       "      <td>97.368</td>\n",
       "      <td>95.977</td>\n",
       "      <td>93.724</td>\n",
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       "      <th>993</th>\n",
       "      <td>61215</td>\n",
       "      <td>190067.0</td>\n",
       "      <td>69310</td>\n",
       "      <td>7005058.0</td>\n",
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       "      <td>0.0643</td>\n",
       "      <td>9.114</td>\n",
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       "      <td>99.022</td>\n",
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      ],
      "text/plain": [
       "     transactions      size  sentbyaddress  difficulty      hashrate  \\\n",
       "989         52572  139256.0          48809   6695826.0  6.550211e+13   \n",
       "990         63095  175443.0          62276   6695826.0  6.184560e+13   \n",
       "991         63766  184209.0          69174   6695826.0  6.027037e+13   \n",
       "992         66738  221568.0          71753   6695826.0  5.527246e+13   \n",
       "993         61215  190067.0          69310   7005058.0  5.181545e+13   \n",
       "\n",
       "     mining_profitability  sentinusdUSD  transactionfeesUSD  \\\n",
       "989                7220.0   136779480.0              0.0777   \n",
       "990                7990.0   278422775.0              0.1050   \n",
       "991                8852.0   284039909.0              0.1330   \n",
       "992                8777.0   282809750.0              0.1410   \n",
       "993                9950.0   264855827.0              0.1200   \n",
       "\n",
       "     median_transaction_feeUSD  confirmationtime  ...  price3rsiUSD  \\\n",
       "989                     0.0474             7.273  ...        99.456   \n",
       "990                     0.0539             7.956  ...        99.811   \n",
       "991                     0.0600             8.229  ...        99.902   \n",
       "992                     0.0618             9.351  ...        99.919   \n",
       "993                     0.0643             9.114  ...        99.941   \n",
       "\n",
       "     price7rsiUSD  price14rsiUSD  price30rsiUSD  price90rsiUSD  price3rocUSD  \\\n",
       "989        96.992         95.075         93.774         91.450         9.383   \n",
       "990        98.201         96.416         94.987         92.645        23.206   \n",
       "991        98.752         97.190         95.785         93.508        34.759   \n",
       "992        98.869         97.368         95.977         93.724        30.456   \n",
       "993        99.022         97.602         96.233         94.013        19.411   \n",
       "\n",
       "     price7rocUSD  price14rocUSD  price30rocUSD  price90rocUSD  \n",
       "989        36.980        104.747        185.993        606.456  \n",
       "990        50.270        109.704        231.964        716.218  \n",
       "991        51.448        107.789        259.795        808.284  \n",
       "992        44.093         93.098        237.355        834.762  \n",
       "993        48.589         95.718        229.992        876.762  \n",
       "\n",
       "[5 rows x 735 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimators=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimators.append(['minmax',MinMaxScaler(feature_range=(-1,1))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale=Pipeline(estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=scale.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=6, random_state=7,\n",
       "    svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(n_components=6,random_state=7)\n",
    "pca.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.43738885, 0.12402112, 0.09038058, 0.04406626, 0.03002204,\n",
       "       0.02513081])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.43738885, 0.56140997, 0.65179055, 0.69585681, 0.72587885,\n",
       "       0.75100966])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cumsum(pca.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    "#Plotting the Cumulative Summation of the Explained Variance\n",
    "plt.figure()\n",
    "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
    "plt.xlabel('Number of Components')\n",
    "plt.ylabel('Variance (%)') #for each component\n",
    "plt.title('Explained Variance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df_pca=pd.DataFrame(pca.components_).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pca.columns=['comp_1','comp_2','comp_3','comp_4','comp_5','comp_6']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df.iloc[:,1:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "y.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pca['priceUSD']=y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>comp_1</th>\n",
       "      <th>comp_2</th>\n",
       "      <th>comp_3</th>\n",
       "      <th>comp_4</th>\n",
       "      <th>comp_5</th>\n",
       "      <th>comp_6</th>\n",
       "      <th>priceUSD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.074162</td>\n",
       "      <td>0.015329</td>\n",
       "      <td>-0.048046</td>\n",
       "      <td>0.042709</td>\n",
       "      <td>0.007321</td>\n",
       "      <td>-0.014252</td>\n",
       "      <td>94.715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.094841</td>\n",
       "      <td>0.072671</td>\n",
       "      <td>-0.077840</td>\n",
       "      <td>-0.014523</td>\n",
       "      <td>0.027039</td>\n",
       "      <td>-0.053013</td>\n",
       "      <td>107.749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.064880</td>\n",
       "      <td>0.028643</td>\n",
       "      <td>-0.038454</td>\n",
       "      <td>0.019065</td>\n",
       "      <td>0.028725</td>\n",
       "      <td>-0.014173</td>\n",
       "      <td>120.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.066701</td>\n",
       "      <td>-0.118325</td>\n",
       "      <td>0.009314</td>\n",
       "      <td>0.034983</td>\n",
       "      <td>-0.002191</td>\n",
       "      <td>0.012280</td>\n",
       "      <td>123.562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.060540</td>\n",
       "      <td>-0.105832</td>\n",
       "      <td>0.011500</td>\n",
       "      <td>0.031725</td>\n",
       "      <td>-0.005375</td>\n",
       "      <td>0.006382</td>\n",
       "      <td>128.664</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     comp_1    comp_2    comp_3    comp_4    comp_5    comp_6  priceUSD\n",
       "0  0.074162  0.015329 -0.048046  0.042709  0.007321 -0.014252    94.715\n",
       "1  0.094841  0.072671 -0.077840 -0.014523  0.027039 -0.053013   107.749\n",
       "2  0.064880  0.028643 -0.038454  0.019065  0.028725 -0.014173   120.003\n",
       "3  0.066701 -0.118325  0.009314  0.034983 -0.002191  0.012280   123.562\n",
       "4  0.060540 -0.105832  0.011500  0.031725 -0.005375  0.006382   128.664"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pca.to_csv('pca_75_reg.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "one=data['priceUSD'].shift(-1,fill_value=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mudassir/btcpaper/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df['one']=one.loc[interval3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mudassir/btcpaper/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df['difference']=((df['one']-df['priceUSD'])/df['priceUSD'])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "category=[]\n",
    "for x in range(len(df['difference'])):\n",
    "    if df['difference'][x]>=0:\n",
    "        category.append(1)\n",
    "    else:\n",
    "        category.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1332"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mudassir/btcpaper/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df['category']=pd.DataFrame(category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2.658</td>\n",
       "      <td>4.788</td>\n",
       "      <td>-1.656</td>\n",
       "      <td>-7.569</td>\n",
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       "      <td>-1.084483</td>\n",
       "      <td>0</td>\n",
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       "      <td>324331</td>\n",
       "      <td>661326.0</td>\n",
       "      <td>418908</td>\n",
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       "      <td>0.152</td>\n",
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       "      <td>46.750</td>\n",
       "      <td>1.722</td>\n",
       "      <td>-1.875</td>\n",
       "      <td>4.286</td>\n",
       "      <td>-3.716</td>\n",
       "      <td>-11.675</td>\n",
       "      <td>7293.0</td>\n",
       "      <td>-1.285869</td>\n",
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       "      <td>0.369</td>\n",
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       "      <td>46.417</td>\n",
       "      <td>-0.935</td>\n",
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       "      <td>6.402</td>\n",
       "      <td>-0.922</td>\n",
       "      <td>-11.770</td>\n",
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       "            Date  priceUSD  transactions      size  sentbyaddress  \\\n",
       "2461  2019/12/27    7263.0        322059  709273.0         415723   \n",
       "2462  2019/12/28    7361.0        298138  625161.0         376120   \n",
       "2463  2019/12/29    7469.0        279770  552879.0         344377   \n",
       "2464  2019/12/30    7388.0        324331  661326.0         418908   \n",
       "2465  2019/12/31    7293.0        293302  687065.0         375764   \n",
       "\n",
       "        difficulty      hashrate  mining_profitability  sentinusdUSD  \\\n",
       "2461  1.294859e+13  9.725392e+19                 0.137  6.292988e+09   \n",
       "2462  1.294859e+13  9.517417e+19                 0.145  5.033568e+09   \n",
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       "2464  1.294859e+13  1.018722e+20                 0.152  7.289436e+09   \n",
       "2465  1.294859e+13  9.877266e+19                 0.135  5.272493e+09   \n",
       "\n",
       "      transactionfeesUSD  ...  price30rsiUSD  price90rsiUSD  price3rocUSD  \\\n",
       "2461               0.386  ...         42.961         46.220        -1.348   \n",
       "2462               0.341  ...         44.575         46.608         1.080   \n",
       "2463               0.337  ...         46.295         47.031         2.631   \n",
       "2464               0.362  ...         45.205         46.750         1.722   \n",
       "2465               0.369  ...         43.938         46.417        -0.935   \n",
       "\n",
       "      price7rocUSD  price14rocUSD  price30rocUSD  price90rocUSD     one  \\\n",
       "2461         0.717          0.076         -0.081        -11.109  7361.0   \n",
       "2462         2.124          2.180         -2.562         -9.029  7469.0   \n",
       "2463         2.658          4.788         -1.656         -7.569  7388.0   \n",
       "2464        -1.875          4.286         -3.716        -11.675  7293.0   \n",
       "2465        -0.948          6.402         -0.922        -11.770  7238.0   \n",
       "\n",
       "      difference  category  \n",
       "2461    1.349305         1  \n",
       "2462    1.467192         1  \n",
       "2463   -1.084483         0  \n",
       "2464   -1.285869         0  \n",
       "2465   -0.754148         0  \n",
       "\n",
       "[5 rows x 740 columns]"
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     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mudassir/btcpaper/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
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   ],
   "source": [
    "df['priceUSD']=df['category']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mudassir/btcpaper/lib/python3.6/site-packages/pandas/core/frame.py:3940: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n"
     ]
    }
   ],
   "source": [
    "df.drop(columns=['category','one','difference'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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       "      <td>231.964</td>\n",
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       "      <td>69174</td>\n",
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       "      <td>6.027037e+13</td>\n",
       "      <td>8852.0</td>\n",
       "      <td>284039909.0</td>\n",
       "      <td>0.1330</td>\n",
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       "      <td>99.902</td>\n",
       "      <td>98.752</td>\n",
       "      <td>97.190</td>\n",
       "      <td>95.785</td>\n",
       "      <td>93.508</td>\n",
       "      <td>34.759</td>\n",
       "      <td>51.448</td>\n",
       "      <td>107.789</td>\n",
       "      <td>259.795</td>\n",
       "      <td>808.284</td>\n",
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       "         Date  priceUSD  transactions      size  sentbyaddress  difficulty  \\\n",
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       "1  2013/04/02         1         63095  175443.0          62276   6695826.0   \n",
       "2  2013/04/03         1         63766  184209.0          69174   6695826.0   \n",
       "\n",
       "       hashrate  mining_profitability  sentinusdUSD  transactionfeesUSD  ...  \\\n",
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       "2  6.027037e+13                8852.0   284039909.0              0.1330  ...   \n",
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       "\n",
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       "0         9.383        36.980        104.747        185.993        606.456  \n",
       "1        23.206        50.270        109.704        231.964        716.218  \n",
       "2        34.759        51.448        107.789        259.795        808.284  \n",
       "\n",
       "[3 rows x 737 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.iloc[:,2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
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       "      <td>9.383</td>\n",
       "      <td>36.980</td>\n",
       "      <td>104.747</td>\n",
       "      <td>185.993</td>\n",
       "      <td>606.456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63095</td>\n",
       "      <td>175443.0</td>\n",
       "      <td>62276</td>\n",
       "      <td>6695826.0</td>\n",
       "      <td>6.184560e+13</td>\n",
       "      <td>7990.0</td>\n",
       "      <td>278422775.0</td>\n",
       "      <td>0.1050</td>\n",
       "      <td>0.0539</td>\n",
       "      <td>7.956</td>\n",
       "      <td>...</td>\n",
       "      <td>99.811</td>\n",
       "      <td>98.201</td>\n",
       "      <td>96.416</td>\n",
       "      <td>94.987</td>\n",
       "      <td>92.645</td>\n",
       "      <td>23.206</td>\n",
       "      <td>50.270</td>\n",
       "      <td>109.704</td>\n",
       "      <td>231.964</td>\n",
       "      <td>716.218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>63766</td>\n",
       "      <td>184209.0</td>\n",
       "      <td>69174</td>\n",
       "      <td>6695826.0</td>\n",
       "      <td>6.027037e+13</td>\n",
       "      <td>8852.0</td>\n",
       "      <td>284039909.0</td>\n",
       "      <td>0.1330</td>\n",
       "      <td>0.0600</td>\n",
       "      <td>8.229</td>\n",
       "      <td>...</td>\n",
       "      <td>99.902</td>\n",
       "      <td>98.752</td>\n",
       "      <td>97.190</td>\n",
       "      <td>95.785</td>\n",
       "      <td>93.508</td>\n",
       "      <td>34.759</td>\n",
       "      <td>51.448</td>\n",
       "      <td>107.789</td>\n",
       "      <td>259.795</td>\n",
       "      <td>808.284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>66738</td>\n",
       "      <td>221568.0</td>\n",
       "      <td>71753</td>\n",
       "      <td>6695826.0</td>\n",
       "      <td>5.527246e+13</td>\n",
       "      <td>8777.0</td>\n",
       "      <td>282809750.0</td>\n",
       "      <td>0.1410</td>\n",
       "      <td>0.0618</td>\n",
       "      <td>9.351</td>\n",
       "      <td>...</td>\n",
       "      <td>99.919</td>\n",
       "      <td>98.869</td>\n",
       "      <td>97.368</td>\n",
       "      <td>95.977</td>\n",
       "      <td>93.724</td>\n",
       "      <td>30.456</td>\n",
       "      <td>44.093</td>\n",
       "      <td>93.098</td>\n",
       "      <td>237.355</td>\n",
       "      <td>834.762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61215</td>\n",
       "      <td>190067.0</td>\n",
       "      <td>69310</td>\n",
       "      <td>7005058.0</td>\n",
       "      <td>5.181545e+13</td>\n",
       "      <td>9950.0</td>\n",
       "      <td>264855827.0</td>\n",
       "      <td>0.1200</td>\n",
       "      <td>0.0643</td>\n",
       "      <td>9.114</td>\n",
       "      <td>...</td>\n",
       "      <td>99.941</td>\n",
       "      <td>99.022</td>\n",
       "      <td>97.602</td>\n",
       "      <td>96.233</td>\n",
       "      <td>94.013</td>\n",
       "      <td>19.411</td>\n",
       "      <td>48.589</td>\n",
       "      <td>95.718</td>\n",
       "      <td>229.992</td>\n",
       "      <td>876.762</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 735 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   transactions      size  sentbyaddress  difficulty      hashrate  \\\n",
       "0         52572  139256.0          48809   6695826.0  6.550211e+13   \n",
       "1         63095  175443.0          62276   6695826.0  6.184560e+13   \n",
       "2         63766  184209.0          69174   6695826.0  6.027037e+13   \n",
       "3         66738  221568.0          71753   6695826.0  5.527246e+13   \n",
       "4         61215  190067.0          69310   7005058.0  5.181545e+13   \n",
       "\n",
       "   mining_profitability  sentinusdUSD  transactionfeesUSD  \\\n",
       "0                7220.0   136779480.0              0.0777   \n",
       "1                7990.0   278422775.0              0.1050   \n",
       "2                8852.0   284039909.0              0.1330   \n",
       "3                8777.0   282809750.0              0.1410   \n",
       "4                9950.0   264855827.0              0.1200   \n",
       "\n",
       "   median_transaction_feeUSD  confirmationtime  ...  price3rsiUSD  \\\n",
       "0                     0.0474             7.273  ...        99.456   \n",
       "1                     0.0539             7.956  ...        99.811   \n",
       "2                     0.0600             8.229  ...        99.902   \n",
       "3                     0.0618             9.351  ...        99.919   \n",
       "4                     0.0643             9.114  ...        99.941   \n",
       "\n",
       "   price7rsiUSD  price14rsiUSD  price30rsiUSD  price90rsiUSD  price3rocUSD  \\\n",
       "0        96.992         95.075         93.774         91.450         9.383   \n",
       "1        98.201         96.416         94.987         92.645        23.206   \n",
       "2        98.752         97.190         95.785         93.508        34.759   \n",
       "3        98.869         97.368         95.977         93.724        30.456   \n",
       "4        99.022         97.602         96.233         94.013        19.411   \n",
       "\n",
       "   price7rocUSD  price14rocUSD  price30rocUSD  price90rocUSD  \n",
       "0        36.980        104.747        185.993        606.456  \n",
       "1        50.270        109.704        231.964        716.218  \n",
       "2        51.448        107.789        259.795        808.284  \n",
       "3        44.093         93.098        237.355        834.762  \n",
       "4        48.589         95.718        229.992        876.762  \n",
       "\n",
       "[5 rows x 735 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimators=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimators.append(['minmax',MinMaxScaler(feature_range=(-1,1))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale=Pipeline(estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=scale.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=50, random_state=7,\n",
       "    svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(n_components=50,random_state=7)\n",
    "pca.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.43738885, 0.12402112, 0.09038058, 0.04406626, 0.03002204,\n",
       "       0.02513081, 0.01856041, 0.01586082, 0.01563144, 0.0134129 ,\n",
       "       0.01191998, 0.01079576, 0.01024017, 0.00906592, 0.00748592,\n",
       "       0.00725145, 0.00641153, 0.0063237 , 0.00550938, 0.00508766,\n",
       "       0.00446051, 0.00406503, 0.00395771, 0.00367028, 0.00338003,\n",
       "       0.00318193, 0.00302956, 0.0029422 , 0.00275338, 0.00268701,\n",
       "       0.00262686, 0.00230424, 0.00222074, 0.00202925, 0.00190189,\n",
       "       0.00186127, 0.00175622, 0.00168084, 0.00161845, 0.00157558,\n",
       "       0.00147986, 0.00144313, 0.00139659, 0.0013125 , 0.00129639,\n",
       "       0.00120848, 0.00110883, 0.00103141, 0.00100158, 0.00096521])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.43738885, 0.56140997, 0.65179055, 0.69585681, 0.72587885,\n",
       "       0.75100966, 0.76957007, 0.78543089, 0.80106233, 0.81447522,\n",
       "       0.8263952 , 0.83719096, 0.84743113, 0.85649705, 0.86398297,\n",
       "       0.87123441, 0.87764595, 0.88396965, 0.88947903, 0.8945667 ,\n",
       "       0.89902721, 0.90309224, 0.90704995, 0.91072023, 0.91410026,\n",
       "       0.91728219, 0.92031175, 0.92325395, 0.92600733, 0.92869434,\n",
       "       0.9313212 , 0.93362545, 0.93584619, 0.93787544, 0.93977733,\n",
       "       0.9416386 , 0.94339482, 0.94507566, 0.94669411, 0.94826969,\n",
       "       0.94974956, 0.95119269, 0.95258928, 0.95390177, 0.95519816,\n",
       "       0.95640664, 0.95751547, 0.95854687, 0.95954846, 0.96051367])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cumsum(pca.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": [
    "#Plotting the Cumulative Summation of the Explained Variance\n",
    "plt.figure()\n",
    "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
    "plt.xlabel('Number of Components')\n",
    "plt.ylabel('Variance (%)') #for each component\n",
    "plt.title('Explained Variance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df_pca=pd.DataFrame(pca.components_).transpose()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "df_pca.columns=['comp_1','comp_2','comp_3','comp_4','comp_5','comp_6']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df.iloc[:,1:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "y.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pca['priceUSD']=y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>41</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "      <th>priceUSD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.074162</td>\n",
       "      <td>0.015329</td>\n",
       "      <td>-0.048046</td>\n",
       "      <td>0.042709</td>\n",
       "      <td>0.007321</td>\n",
       "      <td>-0.014251</td>\n",
       "      <td>0.001355</td>\n",
       "      <td>-0.044263</td>\n",
       "      <td>-0.014403</td>\n",
       "      <td>-0.036199</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017701</td>\n",
       "      <td>-0.020600</td>\n",
       "      <td>-0.021125</td>\n",
       "      <td>-0.001148</td>\n",
       "      <td>-0.004502</td>\n",
       "      <td>-0.012360</td>\n",
       "      <td>-0.032049</td>\n",
       "      <td>0.007081</td>\n",
       "      <td>0.006557</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.094841</td>\n",
       "      <td>0.072671</td>\n",
       "      <td>-0.077840</td>\n",
       "      <td>-0.014523</td>\n",
       "      <td>0.027039</td>\n",
       "      <td>-0.053013</td>\n",
       "      <td>0.056817</td>\n",
       "      <td>-0.009060</td>\n",
       "      <td>0.047423</td>\n",
       "      <td>-0.009912</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.047544</td>\n",
       "      <td>0.013065</td>\n",
       "      <td>0.065670</td>\n",
       "      <td>0.006482</td>\n",
       "      <td>0.020321</td>\n",
       "      <td>0.007130</td>\n",
       "      <td>0.016320</td>\n",
       "      <td>0.013705</td>\n",
       "      <td>-0.042491</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.064880</td>\n",
       "      <td>0.028643</td>\n",
       "      <td>-0.038454</td>\n",
       "      <td>0.019065</td>\n",
       "      <td>0.028725</td>\n",
       "      <td>-0.014173</td>\n",
       "      <td>-0.002313</td>\n",
       "      <td>-0.031474</td>\n",
       "      <td>-0.009467</td>\n",
       "      <td>-0.034115</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020285</td>\n",
       "      <td>0.006481</td>\n",
       "      <td>-0.012896</td>\n",
       "      <td>0.008115</td>\n",
       "      <td>-0.022120</td>\n",
       "      <td>-0.021993</td>\n",
       "      <td>0.012241</td>\n",
       "      <td>0.021045</td>\n",
       "      <td>-0.033730</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.066701</td>\n",
       "      <td>-0.118325</td>\n",
       "      <td>0.009314</td>\n",
       "      <td>0.034983</td>\n",
       "      <td>-0.002191</td>\n",
       "      <td>0.012280</td>\n",
       "      <td>-0.002472</td>\n",
       "      <td>0.004447</td>\n",
       "      <td>0.012511</td>\n",
       "      <td>-0.012443</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000787</td>\n",
       "      <td>0.002537</td>\n",
       "      <td>0.005720</td>\n",
       "      <td>0.002473</td>\n",
       "      <td>-0.002302</td>\n",
       "      <td>-0.018649</td>\n",
       "      <td>-0.002314</td>\n",
       "      <td>0.012274</td>\n",
       "      <td>-0.009088</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.060540</td>\n",
       "      <td>-0.105832</td>\n",
       "      <td>0.011500</td>\n",
       "      <td>0.031725</td>\n",
       "      <td>-0.005375</td>\n",
       "      <td>0.006381</td>\n",
       "      <td>-0.006996</td>\n",
       "      <td>-0.001401</td>\n",
       "      <td>-0.002304</td>\n",
       "      <td>-0.018730</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017911</td>\n",
       "      <td>-0.013560</td>\n",
       "      <td>0.010858</td>\n",
       "      <td>-0.022299</td>\n",
       "      <td>-0.007560</td>\n",
       "      <td>0.010064</td>\n",
       "      <td>-0.016911</td>\n",
       "      <td>-0.002309</td>\n",
       "      <td>0.002561</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "0  0.074162  0.015329 -0.048046  0.042709  0.007321 -0.014251  0.001355   \n",
       "1  0.094841  0.072671 -0.077840 -0.014523  0.027039 -0.053013  0.056817   \n",
       "2  0.064880  0.028643 -0.038454  0.019065  0.028725 -0.014173 -0.002313   \n",
       "3  0.066701 -0.118325  0.009314  0.034983 -0.002191  0.012280 -0.002472   \n",
       "4  0.060540 -0.105832  0.011500  0.031725 -0.005375  0.006381 -0.006996   \n",
       "\n",
       "          7         8         9  ...        41        42        43        44  \\\n",
       "0 -0.044263 -0.014403 -0.036199  ...  0.017701 -0.020600 -0.021125 -0.001148   \n",
       "1 -0.009060  0.047423 -0.009912  ... -0.047544  0.013065  0.065670  0.006482   \n",
       "2 -0.031474 -0.009467 -0.034115  ...  0.020285  0.006481 -0.012896  0.008115   \n",
       "3  0.004447  0.012511 -0.012443  ... -0.000787  0.002537  0.005720  0.002473   \n",
       "4 -0.001401 -0.002304 -0.018730  ...  0.017911 -0.013560  0.010858 -0.022299   \n",
       "\n",
       "         45        46        47        48        49  priceUSD  \n",
       "0 -0.004502 -0.012360 -0.032049  0.007081  0.006557         1  \n",
       "1  0.020321  0.007130  0.016320  0.013705 -0.042491         1  \n",
       "2 -0.022120 -0.021993  0.012241  0.021045 -0.033730         1  \n",
       "3 -0.002302 -0.018649 -0.002314  0.012274 -0.009088         1  \n",
       "4 -0.007560  0.010064 -0.016911 -0.002309  0.002561         1  \n",
       "\n",
       "[5 rows x 51 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pca.to_csv('pca_75_clas.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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