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    "Lab08\n",
    "\n",
    "Consider the following data corresponding to \n",
    "\n",
    "url=\"https://github.com/masterfloss/data/raw/main/dataBitCoins1.xlsx\"\n",
    "\n",
    "It has the following data:\n",
    "* date - date\n",
    "* ElectPrice - price of electicity\n",
    "* BitcoinsPrice - bitcoins prince\n",
    "* TresBondsPrice - US tresury bonds price\n",
    "* BitcoinGoogleSerch - search index in Google (obtained from google trends)\n",
    "\n",
    "Create a model for price BitcoinsPrice prediction.\n",
    "\n",
    "1. Create a Regression model (OLS) using statsmodels library\n",
    "\n",
    "2. Create a Regression model (OLS) using sklearn library \n",
    "\n",
    "3. Create several Regression models and compare accuracy\n",
    "\n",
    "<code>\n",
    "# split train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, random_state=1)\n",
    "# Create model\n",
    "model = linear_model.LinearRegression()\n",
    "# fit the model\n",
    "model.fit(X_train, y_train) \n",
    "# predict y using the X test\n",
    "y_test_pred=model.predict(X_test)\n",
    "# model acuracy\n",
    "metrics.r2_score(y_test, y_test_pred)\n",
    "</code>"
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a4c63a2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "import pandas as pd\n",
    "import sklearn.metrics as metrics\n",
    "from sklearn import linear_model\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "946fe7cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "url=\"https://github.com/masterfloss/data/raw/main/dataBitCoins1.xlsx\"\n",
    "YX=pd.read_excel(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "12754402",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=YX[['ElectPrice','TresBondsPrice','BitcoinGoogleSerch']]\n",
    "Y=YX['BitcoinsPrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   "outputs": [],
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     "execution_count": 16,
     "metadata": {},
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   "source": [
    "\n"
   ]
  }
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