finalcode.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"welcome to the code\")\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "\n",
    "from scipy.ndimage import gaussian_filter1d\n",
    "from scipy.signal import medfilt\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from numpy import array\n",
    "\n",
    "\n",
    "font = {'family' : 'Arial',\n",
    "        'weight' : 'normal',\n",
    "        'size'   : 10}\n",
    "plt.rc('font', **font)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "n_timestamp = 50\n",
    "train_days = 1000  # number of days to train from\n",
    "testing_days = 100 # number of days to be predicted\n",
    "n_epochs = 50\n",
    "\n",
    "#model_type = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nif filter_on == 1:\\n    dataset['t2m'] = medfilt(dataset['t2m'], 3)\\n    dataset['t2m'] = gaussian_filter1d(dataset['t2m'], 1.2)\\n\""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"project.csv\")\n",
    "'''\n",
    "if filter_on == 1:\n",
    "    dataset['t2m'] = medfilt(dataset['t2m'], 3)\n",
    "    dataset['t2m'] = gaussian_filter1d(dataset['t2m'], 1.2)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                time        t2m       u10       v10        d2m  sf   tp  \\\n",
      "0     01/01/17 12:00  295.18082 -0.026832 -1.329328  291.34882   0  0.0   \n",
      "1     02/01/17 12:00  295.43665 -0.004036 -2.185710  290.84296   0  0.0   \n",
      "2     03/01/17 12:00  296.14456  0.847509 -2.320151  290.34433   0  0.0   \n",
      "3     04/01/17 12:00  294.43980  1.602373 -1.768268  289.98276   0  0.0   \n",
      "4     05/01/17 12:00  295.22455  0.714612 -2.223091  289.77798   0  0.0   \n",
      "...              ...        ...       ...       ...        ...  ..  ...   \n",
      "1090  27/12/19 12:00  289.52295  1.060921 -1.491845  286.83713   0  0.0   \n",
      "1091  28/12/19 12:00  290.90780  0.376872 -2.208812  286.73590   0  0.0   \n",
      "1092  29/12/19 12:00  291.76440  1.231489 -1.822173  286.97748   0  0.0   \n",
      "1093  30/12/19 12:00  291.93250  0.189813 -2.204160  287.38837   0  0.0   \n",
      "1094  31/12/19 12:00  292.03370  2.183758 -0.975096  288.26600   0  0.0   \n",
      "\n",
      "      longitude   latitude  \n",
      "0     88.379997  22.879999  \n",
      "1     88.379997  22.879999  \n",
      "2     88.379997  22.879999  \n",
      "3     88.379997  22.879999  \n",
      "4     88.379997  22.879999  \n",
      "...         ...        ...  \n",
      "1090  88.379997  22.879999  \n",
      "1091  88.379997  22.879999  \n",
      "1092  88.379997  22.879999  \n",
      "1093  88.379997  22.879999  \n",
      "1094  88.379997  22.879999  \n",
      "\n",
      "[1095 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set = dataset[0:train_days].reset_index(drop=True)\n",
    "test_set = dataset[train_days: train_days+testing_days].reset_index(drop=True)\n",
    "training_set = train_set.iloc[:, 1:2].values\n",
    "testing_set = test_set.iloc[:, 1:2].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = MinMaxScaler(feature_range = (0, 1))\n",
    "training_set_scaled = sc.fit_transform(training_set)\n",
    "testing_set_scaled = sc.fit_transform(testing_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_split(sequence, n_timestamp):\n",
    "    X = []\n",
    "    y = []\n",
    "    for i in range(len(sequence)):\n",
    "        end_ix = i + n_timestamp\n",
    "        if end_ix > len(sequence)-1:\n",
    "            break\n",
    "    \n",
    "        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return array(X), array(y)\n",
    "\n",
    "\n",
    "X_train, y_train = data_split(training_set_scaled, n_timestamp)\n",
    "X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "X_test, y_test = data_split(testing_set_scaled, n_timestamp)\n",
    "X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(50, activation='relu', return_sequences=True, input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(LSTM(50, activation='relu'))\n",
    "    model.add(Dense(1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "30/30 [==============================] - 4s 49ms/step - loss: 0.2252\n",
      "Epoch 2/50\n",
      "30/30 [==============================] - 2s 51ms/step - loss: 0.0126\n",
      "Epoch 3/50\n",
      "30/30 [==============================] - 1s 46ms/step - loss: 0.0103\n",
      "Epoch 4/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0108\n",
      "Epoch 5/50\n",
      "30/30 [==============================] - 1s 42ms/step - loss: 0.0107\n",
      "Epoch 6/50\n",
      "30/30 [==============================] - 1s 42ms/step - loss: 0.0099\n",
      "Epoch 7/50\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.0099\n",
      "Epoch 8/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0099\n",
      "Epoch 9/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0098\n",
      "Epoch 10/50\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.0099\n",
      "Epoch 11/50\n",
      "30/30 [==============================] - 1s 41ms/step - loss: 0.0093\n",
      "Epoch 12/50\n",
      "30/30 [==============================] - 1s 41ms/step - loss: 0.0092\n",
      "Epoch 13/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0091\n",
      "Epoch 14/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0079\n",
      "Epoch 15/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0090\n",
      "Epoch 16/50\n",
      "30/30 [==============================] - 1s 42ms/step - loss: 0.0093\n",
      "Epoch 17/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0091\n",
      "Epoch 18/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0080\n",
      "Epoch 19/50\n",
      "30/30 [==============================] - 1s 40ms/step - loss: 0.0083\n",
      "Epoch 20/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0081\n",
      "Epoch 21/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0083\n",
      "Epoch 22/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0078\n",
      "Epoch 23/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0081\n",
      "Epoch 24/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0069\n",
      "Epoch 25/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0072\n",
      "Epoch 26/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0066\n",
      "Epoch 27/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0062\n",
      "Epoch 28/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0076\n",
      "Epoch 29/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0064\n",
      "Epoch 30/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0066\n",
      "Epoch 31/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0063\n",
      "Epoch 32/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0062\n",
      "Epoch 33/50\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.0059\n",
      "Epoch 34/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0071\n",
      "Epoch 35/50\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.0063\n",
      "Epoch 36/50\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.0084\n",
      "Epoch 37/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0062\n",
      "Epoch 38/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0063\n",
      "Epoch 39/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0062\n",
      "Epoch 40/50\n",
      "30/30 [==============================] - 1s 40ms/step - loss: 0.0062\n",
      "Epoch 41/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0060\n",
      "Epoch 42/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0068\n",
      "Epoch 43/50\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.0058\n",
      "Epoch 44/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0070\n",
      "Epoch 45/50\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.0056\n",
      "Epoch 46/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0055\n",
      "Epoch 47/50\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.0061\n",
      "Epoch 48/50\n",
      "30/30 [==============================] - 1s 41ms/step - loss: 0.0068\n",
      "Epoch 49/50\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.0063\n",
      "Epoch 50/50\n",
      "30/30 [==============================] - 1s 44ms/step - loss: 0.0059\n"
     ]
    }
   ],
   "source": [
    "# Start training\n",
    "#\n",
    "model.compile(optimizer = 'adam', loss = 'mean_squared_error')\n",
    "history = model.fit(X_train, y_train, epochs = n_epochs, batch_size = 32)\n",
    "loss = history.history['loss']\n",
    "epochs = range(len(loss))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "y_predicted = model.predict(X_test)\n",
    "\n",
    "#\n",
    "# 'De-normalize' the data\n",
    "#\n",
    "y_predicted_descaled = sc.inverse_transform(y_predicted)\n",
    "y_train_descaled = sc.inverse_transform(y_train)\n",
    "y_test_descaled = sc.inverse_transform(y_test)\n",
    "y_pred = y_predicted.ravel()\n",
    "y_pred = [round(yx, 2) for yx in y_pred]\n",
    "y_tested = y_test.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'All data')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(25,7))\n",
    "plt.plot(dataset['t2m'], color = 'black', linewidth=0.5, label = 'True value')\n",
    "plt.grid()\n",
    "plt.ylabel(\"Temperature\")\n",
    "plt.xlabel(\"Day\")\n",
    "plt.title(\"All data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Predicted data (n days)')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1224 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,17))\n",
    "plt.subplot(3, 2, 3)\n",
    "plt.plot(y_test_descaled, color = 'red', linewidth=0.5, label = 'True value')\n",
    "plt.plot(y_predicted_descaled, color = 'blue',  linewidth=0.5, label = 'Predicted')\n",
    "plt.grid()\n",
    "plt.legend(frameon=False)\n",
    "plt.ylabel(\"Temperature\")\n",
    "plt.xlabel(\"Day\")\n",
    "plt.title(\"Predicted data (n days)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Predicted data (first 30 days)')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.subplot(3, 2, 4)\n",
    "plt.plot(y_test_descaled[0:75], color = 'black', linewidth=0.5, label = 'True value')\n",
    "plt.plot(y_predicted_descaled[0:75], color = 'red',linewidth=0.5, label = 'Predicted')\n",
    "plt.grid()\n",
    "plt.legend(frameon=False)\n",
    "plt.ylabel(\"Temperature\")\n",
    "plt.xlabel(\"Day\")\n",
    "plt.title(\"Predicted data (first 30 days)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Training curve')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.subplot(3, 3, 7)\n",
    "plt.plot(epochs, loss, color='black')\n",
    "plt.grid()\n",
    "plt.ylabel(\"Loss (MSE)\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.title(\"Training curve\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Residual plot')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.subplot(3, 3, 8)\n",
    "plt.plot(y_test_descaled-y_predicted_descaled, color='black')\n",
    "plt.grid()\n",
    "plt.ylabel(\"Residual\")\n",
    "plt.xlabel(\"Day\")\n",
    "plt.title(\"Residual plot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Scatter plot')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.subplot(3, 3, 9)\n",
    "plt.scatter(y_predicted_descaled, y_test_descaled, s=2, color='black')\n",
    "plt.ylabel(\"Y true\")\n",
    "plt.xlabel(\"Y predicted\")\n",
    "plt.title(\"Scatter plot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots_adjust(hspace = 0.5, wspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmse=1.4\n",
      "mse=1.96\n",
      "r^2=0.8\n"
     ]
    }
   ],
   "source": [
    "rmse = mean_squared_error(y_test_descaled, y_predicted_descaled,squared=False)\n",
    "mse= mean_squared_error(y_test_descaled, y_predicted_descaled)\n",
    "r2 = r2_score(y_test_descaled, y_predicted_descaled)\n",
    "print(\"rmse=\" + str(round(rmse,2)))\n",
    "print(\"mse=\" + str(round(mse,2)))\n",
    "print(\"r^2=\" + str(round(r2,2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.DataFrame(y_test_descaled,columns=['Actual'])\n",
    "pred = pd.DataFrame(y_predicted_descaled,columns=['Predict'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = pd.concat([test,pred],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Actual</th>\n",
       "      <th>Predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>298.60657</td>\n",
       "      <td>299.298096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>299.21793</td>\n",
       "      <td>298.815643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>298.16077</td>\n",
       "      <td>299.079132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>298.53003</td>\n",
       "      <td>298.533325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>297.80307</td>\n",
       "      <td>298.653168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>298.52940</td>\n",
       "      <td>298.273621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>297.57720</td>\n",
       "      <td>298.613403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>298.65780</td>\n",
       "      <td>298.146545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>298.96550</td>\n",
       "      <td>298.657959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>299.67465</td>\n",
       "      <td>298.896851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>298.04395</td>\n",
       "      <td>299.317078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>298.56876</td>\n",
       "      <td>298.459747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>298.86243</td>\n",
       "      <td>298.588470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>299.83960</td>\n",
       "      <td>298.750549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>298.25262</td>\n",
       "      <td>299.318665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>295.78030</td>\n",
       "      <td>298.534058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>296.62970</td>\n",
       "      <td>297.165924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>294.80185</td>\n",
       "      <td>297.244415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>295.94244</td>\n",
       "      <td>296.239410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>298.62686</td>\n",
       "      <td>296.578339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Actual     Predict\n",
       "0   298.60657  299.298096\n",
       "1   299.21793  298.815643\n",
       "2   298.16077  299.079132\n",
       "3   298.53003  298.533325\n",
       "4   297.80307  298.653168\n",
       "5   298.52940  298.273621\n",
       "6   297.57720  298.613403\n",
       "7   298.65780  298.146545\n",
       "8   298.96550  298.657959\n",
       "9   299.67465  298.896851\n",
       "10  298.04395  299.317078\n",
       "11  298.56876  298.459747\n",
       "12  298.86243  298.588470\n",
       "13  299.83960  298.750549\n",
       "14  298.25262  299.318665\n",
       "15  295.78030  298.534058\n",
       "16  296.62970  297.165924\n",
       "17  294.80185  297.244415\n",
       "18  295.94244  296.239410\n",
       "19  298.62686  296.578339"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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