data_prep.ipynb

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   "cell_type": "code",
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   "source": [
    "steps = 50"
   ]
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  {
   "cell_type": "code",
   "execution_count": 19,
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     "text": [
      "(26538, 50, 9)\n"
     ]
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   "source": [
    "# Prepare the training data\n",
    "x_train = []\n",
    "y_train = []\n",
    "\n",
    "for i in range(steps, training_set_scaled.shape[0]-steps):\n",
    "    x_train.append(training_set_scaled[i-steps:i, :])\n",
    "    y_train.append(training_set_scaled[i, :])\n",
    "\n",
    "x_train, y_train = np.array(x_train), np.array(y_train)\n",
    "print(x_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6611, 50, 9)\n"
     ]
    }
   ],
   "source": [
    "# Prepare the test data\n",
    "x_test = []\n",
    "y_test = []\n",
    "\n",
    "for i in range(steps, test_set_scaled.shape[0]):\n",
    "    x_test.append(test_set_scaled[i-steps:i, :])\n",
    "    y_test.append(test_set_scaled[i, :])\n",
    "\n",
    "x_test, y_test = np.array(x_test), np.array(y_test)\n",
    "print(x_test.shape)"
   ]
  }
 ],
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