Bitcoin_LinearRegression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1655 entries, 2013-04-28 to 2017-11-07\n",
      "Data columns (total 6 columns):\n",
      "Open          1655 non-null float64\n",
      "High          1655 non-null float64\n",
      "Low           1655 non-null float64\n",
      "Close         1655 non-null float64\n",
      "Volume        1655 non-null object\n",
      "Market Cap    1655 non-null object\n",
      "dtypes: float64(4), object(2)\n",
      "memory usage: 90.5+ KB\n",
      "              Close\n",
      "Date               \n",
      "2017-11-03  7207.76\n",
      "2017-11-04  7379.95\n",
      "2017-11-05  7407.41\n",
      "2017-11-06  7022.76\n",
      "2017-11-07  7144.38\n"
     ]
    }
   ],
   "source": [
    "# import quandl\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import preprocessing, cross_validation, svm\n",
    "\n",
    "# df = quandl.get(\"WIKI/AMZN\")\n",
    "df = pd.read_csv('bitcoin_price.csv', parse_dates=['Date'],)\n",
    "df=df.iloc[::-1]\n",
    "df.index = df['Date']\n",
    "del df['Date']\n",
    "df.info()\n",
    "df = df[['Close']]\n",
    "print(df.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-04-28</th>\n",
       "      <td>134.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-29</th>\n",
       "      <td>144.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-30</th>\n",
       "      <td>139.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-01</th>\n",
       "      <td>116.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-02</th>\n",
       "      <td>105.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Close\n",
       "Date              \n",
       "2013-04-28  134.21\n",
       "2013-04-29  144.54\n",
       "2013-04-30  139.00\n",
       "2013-05-01  116.99\n",
       "2013-05-02  105.21"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confidence:  0.9036278554322356\n",
      "[5691.10004813 5703.08055399 5756.54220957 6502.08536461 6742.77697197\n",
      " 6964.57855404 6780.00426102 6836.96273426 6692.66793539 6674.85940009\n",
      " 6816.45047396 7180.46799077 7204.68135018 7176.82697445 7082.97767572\n",
      " 6597.89336189 6867.25647076 7052.34747566 6903.42630292 6870.00826198\n",
      " 7351.58374159 7323.56113408 7729.56449594 8088.75135743 8462.69454911\n",
      " 8618.02054596 8824.93361571 8857.93107721 8395.71426856 8541.85961698]\n"
     ]
    }
   ],
   "source": [
    "df = df[['Close']]\n",
    "\n",
    "forecast_out = int(30)\n",
    "\n",
    "df['Prediction'] = df[['Close']].shift(-forecast_out)\n",
    "\n",
    "X = np.array(df.drop(['Prediction'],1))\n",
    "X = preprocessing.scale(X)\n",
    "\n",
    "X_forecast = X[-forecast_out:]\n",
    "X = X[:-forecast_out]\n",
    "\n",
    "y = np.array(df['Prediction'])\n",
    "y = y[:-forecast_out]\n",
    "\n",
    "X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size = 0.2)\n",
    "\n",
    "clf = LinearRegression()\n",
    "clf.fit(X_train,y_train)\n",
    "\n",
    "confidence = clf.score(X_test, y_test)\n",
    "print(\"confidence: \", confidence)\n",
    "\n",
    "forecast_prediction = clf.predict(X_forecast)\n",
    "print(forecast_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc10b14470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# print(X_test)\n",
    "# print(y_test)\n",
    "# plt.plot(X_test,forecast_set,color='blue', linewidth=1)\n",
    "plt.scatter(X_test, y_test, color='darkorange', label='data')\n",
    "# plt.plot(X_test, y_test, color='darkorange', linewidth=1)\n",
    "# plt.plot(X_test, forecast_prediction, color='blue', linew/idth=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9036278554322356"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dict_new = {\n",
    "#     'Date': X_train,\n",
    "#     'Close': y_train\n",
    "# }\n",
    "# df1=pd.DataFrame(dict_new).set_index('Date')\n",
    "# df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc10ae9278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(df['Close'], label='Train')\n",
    "# plt.plot(X_test['Close'], label='Test')\n",
    "# plt.plot(y_hat_avg['avg_forecast'], label='Average Forecast')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3629.001658095474\n"
     ]
    }
   ],
   "source": [
    "rms = sqrt(mean_squared_error(test.Close, y_hat_avg.avg_forecast))\n",
    "print(rms)"
   ]
  }
 ],
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