diff --git "a/file_info.ipynb" "b/file_info.ipynb"
--- "a/file_info.ipynb"
+++ "b/file_info.ipynb"
@@ -16,7 +16,8 @@
     "from keras.models import Sequential\n",
     "from keras.layers import LSTM, Dense\n",
     "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import MinMaxScaler\n"
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from keras.callbacks import ModelCheckpoint\n"
    ]
   },
   {
@@ -462,9 +463,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "correlation_matrix = sorted.loc[:, sorted.columns != 'date'].corr()\n",
     "plt.figure(figsize=(15, 10))\n",
@@ -475,7 +487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -519,7 +531,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -544,21 +556,21 @@
        "rtu_004_fltrd_gnd_lvl_plenum_press_tn         0\n",
        "rtu_004_fltrd_lvl2_plenum_press_tn            0\n",
        "zone_047_cooling_sp                           0\n",
-       "Unnamed: 47_x                                 0\n",
+       "Unnamed: 47_x                            394570\n",
        "zone_047_heating_sp                           0\n",
-       "Unnamed: 47_y                                 0\n",
-       "hvac_S                                        0\n",
+       "Unnamed: 47_y                            394570\n",
+       "hvac_S                                    13035\n",
        "hp_hws_temp                                   0\n",
-       "aru_001_cwr_temp                         667858\n",
-       "aru_001_cws_fr_gpm                       667858\n",
-       "aru_001_cws_temp                         667858\n",
-       "aru_001_hwr_temp                              0\n",
-       "aru_001_hws_fr_gpm                            0\n",
-       "aru_001_hws_temp                              0\n",
+       "aru_001_cwr_temp                         524350\n",
+       "aru_001_cws_fr_gpm                       524350\n",
+       "aru_001_cws_temp                         524350\n",
+       "aru_001_hwr_temp                         299165\n",
+       "aru_001_hws_fr_gpm                       299165\n",
+       "aru_001_hws_temp                         299165\n",
        "dtype: int64"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -566,25 +578,26 @@
    "source": [
     "final_df = sorted.copy()\n",
     "final_df['date'] = pd.to_datetime(final_df['date'], format = \"%Y-%m-%d %H:%M:%S\")\n",
-    "final_df = final_df[ (final_df.date.dt.date >date(2020, 1, 1)) & (final_df.date.dt.date< date(2020, 12, 30))]\n",
+    "final_df = final_df[ (final_df.date.dt.date >date(2019, 4, 1)) & (final_df.date.dt.date< date(2020, 2, 15))]\n",
     "final_df.isna().sum()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib qt\n",
-    "for i in final_df.columns[11:14]:\n",
-    "    plt.plot(final_df['date'],final_df[i])\n",
-    "plt.show()"
+    "for i in final_df.columns[6:8]:\n",
+    "    plt.plot(final_df[i].to_list(), alpha= 0.5)\n",
+    "plt.show()\n",
+    "# [:int(len(final_df)*.51)]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -600,17 +613,37 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/2\n",
-      "\u001b[1m12174/12174\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m811s\u001b[0m 66ms/step - loss: 0.0019 - val_loss: 9.6280e-04\n",
-      "Epoch 2/2\n",
-      "\u001b[1m12174/12174\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m814s\u001b[0m 67ms/step - loss: 7.9909e-04 - val_loss: 7.6609e-04\n",
-      "\u001b[1m24348/24348\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m120s\u001b[0m 5ms/step\n",
-      "\u001b[1m10434/10434\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 6ms/step\n"
+      "\u001b[1m 311/4014\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:19\u001b[0m 21ms/step - loss: 0.0363"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[5], line 50\u001b[0m\n\u001b[0;32m     46\u001b[0m model\u001b[38;5;241m.\u001b[39madd(Dense(units\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m))\n\u001b[0;32m     48\u001b[0m model\u001b[38;5;241m.\u001b[39mcompile(optimizer\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124madam\u001b[39m\u001b[38;5;124m'\u001b[39m, loss\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmean_squared_error\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m---> 50\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_test\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m64\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m     52\u001b[0m train_predict \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mpredict(X_train)\n\u001b[0;32m     53\u001b[0m test_predict \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mpredict(X_test)\n",
+      "File \u001b[1;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:118\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    116\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    117\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 118\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    119\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    120\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
+      "File \u001b[1;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py:323\u001b[0m, in \u001b[0;36mTensorFlowTrainer.fit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001b[0m\n\u001b[0;32m    321\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step, iterator \u001b[38;5;129;01min\u001b[39;00m epoch_iterator\u001b[38;5;241m.\u001b[39menumerate_epoch():\n\u001b[0;32m    322\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[1;32m--> 323\u001b[0m     logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    324\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_end(\n\u001b[0;32m    325\u001b[0m         step, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pythonify_logs(logs)\n\u001b[0;32m    326\u001b[0m     )\n\u001b[0;32m    327\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstop_training:\n",
+      "File \u001b[1;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
+      "File \u001b[1;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:833\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    830\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    832\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[1;32m--> 833\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    835\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[0;32m    836\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
+      "File \u001b[1;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:878\u001b[0m, in \u001b[0;36mFunction._call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    875\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[0;32m    876\u001b[0m \u001b[38;5;66;03m# In this case we have not created variables on the first call. So we can\u001b[39;00m\n\u001b[0;32m    877\u001b[0m \u001b[38;5;66;03m# run the first trace but we should fail if variables are created.\u001b[39;00m\n\u001b[1;32m--> 878\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mtracing_compilation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    879\u001b[0m \u001b[43m    \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_variable_creation_config\u001b[49m\n\u001b[0;32m    880\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    881\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_created_variables:\n\u001b[0;32m    882\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating variables on a non-first call to a function\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    883\u001b[0m                    \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m decorated with tf.function.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\tracing_compilation.py:139\u001b[0m, in \u001b[0;36mcall_function\u001b[1;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[0;32m    137\u001b[0m bound_args \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mbind(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    138\u001b[0m flat_inputs \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39munpack_inputs(bound_args)\n\u001b[1;32m--> 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# pylint: disable=protected-access\u001b[39;49;00m\n\u001b[0;32m    140\u001b[0m \u001b[43m    \u001b[49m\u001b[43mflat_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\n\u001b[0;32m    141\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\concrete_function.py:1322\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[1;34m(self, tensor_inputs, captured_inputs)\u001b[0m\n\u001b[0;32m   1318\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[0;32m   1319\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[0;32m   1320\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[0;32m   1321\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[1;32m-> 1322\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_preflattened\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1323\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[0;32m   1324\u001b[0m     args,\n\u001b[0;32m   1325\u001b[0m     possible_gradient_type,\n\u001b[0;32m   1326\u001b[0m     executing_eagerly)\n\u001b[0;32m   1327\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
+      "File \u001b[1;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\atomic_function.py:216\u001b[0m, in \u001b[0;36mAtomicFunction.call_preflattened\u001b[1;34m(self, args)\u001b[0m\n\u001b[0;32m    214\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcall_preflattened\u001b[39m(\u001b[38;5;28mself\u001b[39m, args: Sequence[core\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[0;32m    215\u001b[0m \u001b[38;5;250m  \u001b[39m\u001b[38;5;124;03m\"\"\"Calls with flattened tensor inputs and returns the structured output.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 216\u001b[0m   flat_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    217\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mpack_output(flat_outputs)\n",
+      "File \u001b[1;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\atomic_function.py:251\u001b[0m, in \u001b[0;36mAtomicFunction.call_flat\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    249\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[0;32m    250\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[1;32m--> 251\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_bound_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    252\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    253\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflat_outputs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    255\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    256\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    257\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\n\u001b[0;32m    258\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    259\u001b[0m         \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[0;32m    260\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mfunction_call_options\u001b[38;5;241m.\u001b[39mas_attrs(),\n\u001b[0;32m    261\u001b[0m     )\n",
+      "File \u001b[1;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\context.py:1500\u001b[0m, in \u001b[0;36mContext.call_function\u001b[1;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[0;32m   1498\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[0;32m   1499\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1500\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1501\u001b[0m \u001b[43m      \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1502\u001b[0m \u001b[43m      \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1503\u001b[0m \u001b[43m      \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtensor_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1504\u001b[0m \u001b[43m      \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1505\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1506\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1507\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1508\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[0;32m   1509\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m   1510\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1514\u001b[0m       cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[0;32m   1515\u001b[0m   )\n",
+      "File \u001b[1;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     54\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
    ],
    "source": [
     "\n",
-    "dataset = final_df[['rtu_004_oa_temp','rtu_004_ra_temp','hp_hws_temp','rtu_004_ma_temp','rtu_004_sa_temp']].values\n",
+    "dataset = final_df[['rtu_004_oa_temp','rtu_004_ra_temp','hp_hws_temp','rtu_004_oa_flow_tn','rtu_004_oadmpr_pct',\n",
+    "                    'rtu_004_sat_sp_tn','rtu_004_rf_vfd_spd_fbk_tn','rtu_004_ma_temp','rtu_004_sa_temp','rtu_004_fltrd_sa_flow_tn',\n",
+    "                    'rtu_004_sf_vfd_spd_fbk_tn']].values\n",
     "\n",
     "# dataset = final_df[['hvac_S','rtu_004_ra_temp','rtu_004_oa_temp','rtu_004_ma_temp','rtu_004_fltrd_sa_flow_tn',\n",
     "#                     'rtu_004_sf_vfd_spd_fbk_tn','rtu_004_rf_vfd_spd_fbk_tn','zone_047_temp']].values\n",
@@ -621,26 +654,26 @@
     "\n",
     "scaler = MinMaxScaler(feature_range=(0, 1))\n",
     "dataset = scaler.fit_transform(dataset)\n",
-    "train_size = int(len(dataset)* 0.30)\n",
+    "train_size = int(len(dataset)* 0.51)\n",
     "test_size = len(dataset) - train_size\n",
     "test,train = dataset[0:train_size,:],dataset[train_size:len(dataset),:]\n",
     "\n",
     "def create_dataset(dataset,time_step):\n",
-    "    # x1,x2,x3,x4,x5,x6,x7, Y = [],[],[],[],[],[],[],[]\n",
+    "    x1,x2,x3,x4,x5,x6,x7, Y = [],[],[],[],[],[],[],[]\n",
     "    x1,x2,x3,Y = [],[],[],[]\n",
     "    for i in range(len(dataset)-time_step-1):\n",
     "        x1.append(dataset[i:(i+time_step), 0])\n",
     "        x2.append(dataset[i:(i+time_step), 1])\n",
     "        x3.append(dataset[i:(i+time_step), 2])\n",
-    "        # x4.append(dataset[i:(i+time_step), 3])\n",
-    "        # x5.append(dataset[i:(i+time_step), 4])\n",
-    "        # x6.append(dataset[i:(i+time_step), 5])\n",
-    "        # x7.append(dataset[i:(i+time_step), 6])\n",
-    "        Y.append([dataset[i + time_step, 3],dataset[i + time_step, 4]])\n",
-    "    # x1,x2,x3,x4,x5,x6,x7,Y =  np.array(x1),np.array(x2),np.array(x3), np.array(x4),np.array(x5),np.array(x6),np.array(x7),np.array(Y)\n",
-    "    x1,x2,x3,Y = np.array(x1),np.array(x2),np.array(x3),np.array(Y)\n",
+    "        x4.append(dataset[i:(i+time_step), 3])\n",
+    "        x5.append(dataset[i:(i+time_step), 4])\n",
+    "        x6.append(dataset[i:(i+time_step), 5])\n",
+    "        x7.append(dataset[i:(i+time_step), 6])\n",
+    "        Y.append([dataset[i + time_step-1, 7],dataset[i + time_step-1, 8],dataset[i + time_step-1, 9],dataset[i + time_step-1, 10]])\n",
+    "    x1,x2,x3,x4,x5,x6,x7,Y =  np.array(x1),np.array(x2),np.array(x3), np.array(x4),np.array(x5),np.array(x6),np.array(x7),np.array(Y)\n",
+    "    # x1,x2,x3,Y = np.array(x1),np.array(x2),np.array(x3),np.array(Y)\n",
     "    # Y = np.reshape(Y,(len(Y),1))\n",
-    "    return np.stack([x1,x2,x3],axis=2),Y\n",
+    "    return np.stack([x1,x2,x3,x4,x5,x6,x7],axis=2),Y\n",
     "\n",
     "\n",
     "\n",
@@ -653,11 +686,11 @@
     "model = Sequential()\n",
     "model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], X_train.shape[2])))\n",
     "model.add(LSTM(units=50))\n",
-    "model.add(Dense(units=2))\n",
+    "model.add(Dense(units=4))\n",
     "\n",
     "model.compile(optimizer='adam', loss='mean_squared_error')\n",
     "\n",
-    "model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5, batch_size=64, verbose=1)\n",
+    "model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=2, batch_size=64, verbose=1)\n",
     "\n",
     "train_predict = model.predict(X_train)\n",
     "test_predict = model.predict(X_test)\n"
@@ -665,18 +698,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(256871, 60, 7)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
    "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib qt\n",
+    "#'rtu_004_ma_temp','rtu_004_sa_temp','rtu_004_fltrd_sa_flow_tn','rtu_004_sf_vfd_spd_fbk_tn'\n",
+    "\n",
+    "plt.plot(y_test[:,0], label='Original Testing Data', color='blue')\n",
+    "plt.plot(test_predict[:,0], label='Predicted Testing Data', color='red',alpha=0.8)\n",
+    "anomalies = np.where(abs(test_predict[:,0] - y_test[:,0]) > 0.1)[0]\n",
+    "plt.scatter(anomalies,test_predict[anomalies,0], color='black',marker =\"o\",s=100 )\n",
+    "\n",
+    "# plt.plot(y_test[:,1], label='Original Testing Data', color='green')\n",
+    "# plt.plot(test_predict[:,1], label='Predicted Testing Data', color='orange',alpha=0.8)\n",
+    "# anomalies = np.where(abs(test_predict[:,1] - y_test[:,1]) > 0.03)[0]\n",
+    "# plt.scatter(anomalies,test_predict[anomalies,1], color='black',marker =\"o\",s=100 )\n",
     "\n",
-    "# plt.plot(y_test[:,0], label='Original Testing Data', color='blue')\n",
-    "# plt.plot(test_predict[:,0], label='Predicted Testing Data', color='red')\n",
-    "plt.plot(y_test[:,1], label='Original Testing Data', color='green')\n",
-    "plt.plot(test_predict[:,1], label='Predicted Testing Data', color='orange')\n",
-    "anomalies = np.where(abs(test_predict[:,1] - y_test[:,0]) > 0.5)[0]\n",
-    "plt.scatter(anomalies,test_predict[anomalies,1], color='black',marker =\"o\",s=100 )\n",
     "plt.title('Testing Data - Predicted vs Actual')\n",
     "plt.xlabel('Time')\n",
     "plt.ylabel('Value')\n",
@@ -688,7 +746,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "LSTM autoencoder"
+    "LSTM "
    ]
   },
   {
@@ -700,7 +758,227 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 246,
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# final_df = final_df[ (final_df.date.dt.date >date(2019, 4, 1)) & (final_df.date.dt.date< date(2020, 1, 15))]\n",
+    "testdataset_df = final_df[(final_df.date.dt.date <date(2019, 11, 8))]\n",
+    "\n",
+    "traindataset_df = final_df[ (final_df.date.dt.date >date(2019, 11, 8))]\n",
+    "\n",
+    "testdataset = testdataset_df[['rtu_004_oa_temp','rtu_004_ra_temp','hp_hws_temp','rtu_004_oa_flow_tn','rtu_004_oadmpr_pct',\n",
+    "                    'rtu_004_sat_sp_tn','rtu_004_rf_vfd_spd_fbk_tn','rtu_004_ma_temp','rtu_004_sa_temp','rtu_004_fltrd_sa_flow_tn',\n",
+    "                    'rtu_004_sf_vfd_spd_fbk_tn']].values\n",
+    "\n",
+    "\n",
+    "traindataset = traindataset_df[['rtu_004_oa_temp','rtu_004_ra_temp','hp_hws_temp','rtu_004_oa_flow_tn','rtu_004_oadmpr_pct',\n",
+    "                    'rtu_004_sat_sp_tn','rtu_004_rf_vfd_spd_fbk_tn','rtu_004_ma_temp','rtu_004_sa_temp','rtu_004_fltrd_sa_flow_tn',\n",
+    "                    'rtu_004_sf_vfd_spd_fbk_tn']].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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>rtu_004_oa_temp</th>\n",
+       "      <th>rtu_004_ra_temp</th>\n",
+       "      <th>hp_hws_temp</th>\n",
+       "      <th>rtu_004_oa_flow_tn</th>\n",
+       "      <th>rtu_004_oadmpr_pct</th>\n",
+       "      <th>rtu_004_sat_sp_tn</th>\n",
+       "      <th>rtu_004_rf_vfd_spd_fbk_tn</th>\n",
+       "      <th>rtu_004_ma_temp</th>\n",
+       "      <th>rtu_004_sa_temp</th>\n",
+       "      <th>rtu_004_fltrd_sa_flow_tn</th>\n",
+       "      <th>rtu_004_sf_vfd_spd_fbk_tn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>872640</th>\n",
+       "      <td>48.8</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>119.8</td>\n",
+       "      <td>5507.795320</td>\n",
+       "      <td>32.2</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>86.9</td>\n",
+       "      <td>70.2</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>11551.751</td>\n",
+       "      <td>83.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>872641</th>\n",
+       "      <td>48.6</td>\n",
+       "      <td>68.5</td>\n",
+       "      <td>119.6</td>\n",
+       "      <td>7246.995886</td>\n",
+       "      <td>54.8</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>86.2</td>\n",
+       "      <td>66.5</td>\n",
+       "      <td>68.5</td>\n",
+       "      <td>11429.937</td>\n",
+       "      <td>83.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>872642</th>\n",
+       "      <td>48.7</td>\n",
+       "      <td>66.8</td>\n",
+       "      <td>119.8</td>\n",
+       "      <td>7333.638704</td>\n",
+       "      <td>33.6</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>63.4</td>\n",
+       "      <td>66.8</td>\n",
+       "      <td>11511.290</td>\n",
+       "      <td>83.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>872643</th>\n",
+       "      <td>48.6</td>\n",
+       "      <td>67.3</td>\n",
+       "      <td>120.3</td>\n",
+       "      <td>7293.291732</td>\n",
+       "      <td>25.2</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>86.0</td>\n",
+       "      <td>67.8</td>\n",
+       "      <td>67.3</td>\n",
+       "      <td>11098.563</td>\n",
+       "      <td>80.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>872644</th>\n",
+       "      <td>48.6</td>\n",
+       "      <td>68.6</td>\n",
+       "      <td>120.8</td>\n",
+       "      <td>6041.782793</td>\n",
+       "      <td>25.2</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>85.1</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>68.6</td>\n",
+       "      <td>11429.937</td>\n",
+       "      <td>82.9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        rtu_004_oa_temp  rtu_004_ra_temp  hp_hws_temp  rtu_004_oa_flow_tn  \\\n",
+       "872640             48.8             69.0        119.8         5507.795320   \n",
+       "872641             48.6             68.5        119.6         7246.995886   \n",
+       "872642             48.7             66.8        119.8         7333.638704   \n",
+       "872643             48.6             67.3        120.3         7293.291732   \n",
+       "872644             48.6             68.6        120.8         6041.782793   \n",
+       "\n",
+       "        rtu_004_oadmpr_pct  rtu_004_sat_sp_tn  rtu_004_rf_vfd_spd_fbk_tn  \\\n",
+       "872640                32.2               68.0                       86.9   \n",
+       "872641                54.8               68.0                       86.2   \n",
+       "872642                33.6               68.0                       91.0   \n",
+       "872643                25.2               68.0                       86.0   \n",
+       "872644                25.2               68.0                       85.1   \n",
+       "\n",
+       "        rtu_004_ma_temp  rtu_004_sa_temp  rtu_004_fltrd_sa_flow_tn  \\\n",
+       "872640             70.2             69.0                 11551.751   \n",
+       "872641             66.5             68.5                 11429.937   \n",
+       "872642             63.4             66.8                 11511.290   \n",
+       "872643             67.8             67.3                 11098.563   \n",
+       "872644             70.0             68.6                 11429.937   \n",
+       "\n",
+       "        rtu_004_sf_vfd_spd_fbk_tn  \n",
+       "872640                       83.5  \n",
+       "872641                       83.0  \n",
+       "872642                       83.2  \n",
+       "872643                       80.1  \n",
+       "872644                       82.9  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# buildingdata = testdataset_df[['rtu_004_oa_temp','rtu_004_ra_temp','hp_hws_temp','rtu_004_oa_flow_tn','rtu_004_oadmpr_pct',\n",
+    "#                     'rtu_004_sat_sp_tn','rtu_004_rf_vfd_spd_fbk_tn','rtu_004_ma_temp','rtu_004_sa_temp','rtu_004_fltrd_sa_flow_tn',\n",
+    "#                     'rtu_004_sf_vfd_spd_fbk_tn']]\n",
+    "# buildingdata.to_csv('buildingdata.csv')\n",
+    "# buildingdata[len(buildingdata)-5:len(buildingdata)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x21c5aa00ad0>]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# plt.plot(testdataset_df['date'],testdataset_df['rtu_004_oa_temp'],c='r',alpha=0.9)\n",
+    "plt.plot(traindataset_df['date'],traindataset_df['rtu_004_oa_temp'],c='r',alpha=0.9)\n",
+    "# plt.plot(traindataset_df['rtu_004_rf_vfd_spd_fbk_tn'])\n",
+    "# plt.plot(testdataset_df['rtu_004_rf_vfd_spd_fbk_tn'])\n",
+    "# plt.plot(traindataset_df['date'],traindataset_df['rtu_004_sf_vfd_spd_fbk_tn'],c='b',alpha=0.6)\n",
+    "# plt.plot(testdataset_df['date'],testdataset_df['rtu_004_sf_vfd_spd_fbk_tn'],c='b',alpha=0.6)\n",
+    "# plt.plot(traindataset_df['rtu_004_fltrd_sa_flow_tn']/1000)\n",
+    "# plt.plot(testdataset_df['rtu_004_fltrd_sa_flow_tn']/1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "traindataset = traindataset.astype('float32')\n",
+    "testdataset = testdataset.astype('float32')\n",
+    "\n",
+    "\n",
+    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "traindataset = scaler.fit_transform(traindataset)\n",
+    "testdataset = scaler.transform(testdataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -715,41 +993,40 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m11487/24348\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m2:07\u001b[0m 10ms/step"
-     ]
-    },
-    {
-     "ename": "",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
-      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
-      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
-      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+      "Epoch 1/5\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 0.0088\n",
+      "Epoch 1: val_loss improved from inf to 0.03342, saving model to model_checkpoint4.keras\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m113s\u001b[0m 34ms/step - loss: 0.0088 - val_loss: 0.0334\n",
+      "Epoch 2/5\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 0.0011\n",
+      "Epoch 2: val_loss did not improve from 0.03342\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━��━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 32ms/step - loss: 0.0011 - val_loss: 0.0432\n",
+      "Epoch 3/5\n",
+      "\u001b[1m3218/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 7.4667e-04\n",
+      "Epoch 3: val_loss did not improve from 0.03342\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m107s\u001b[0m 33ms/step - loss: 7.4661e-04 - val_loss: 0.0500\n",
+      "Epoch 4/5\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 5.4995e-04\n",
+      "Epoch 4: val_loss did not improve from 0.03342\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 34ms/step - loss: 5.4994e-04 - val_loss: 0.0421\n",
+      "Epoch 5/5\n",
+      "\u001b[1m3218/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 3.8050e-04\n",
+      "Epoch 5: val_loss improved from 0.03342 to 0.02837, saving model to model_checkpoint4.keras\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 33ms/step - loss: 3.8049e-04 - val_loss: 0.0284\n",
+      "\u001b[1m6440/6440\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 5ms/step\n",
+      "\u001b[1m9900/9900\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m49s\u001b[0m 5ms/step\n"
      ]
     }
    ],
    "source": [
     "\n",
-    "# dataset = final_df[['zone_047_temp','hvac_S','rtu_004_sa_temp']].values\n",
-    "\n",
-    "# dataset = final_df[['hvac_S','rtu_004_ra_temp','rtu_004_oa_temp','rtu_004_ma_temp','rtu_004_fltrd_sa_flow_tn',\n",
-    "#                     'rtu_004_sf_vfd_spd_fbk_tn','rtu_004_rf_vfd_spd_fbk_tn','zone_047_temp']].values\n",
-    "dataset = final_df[['rtu_004_fltrd_sa_flow_tn','rtu_004_sf_vfd_spd_fbk_tn','rtu_004_rf_vfd_spd_fbk_tn',\n",
-    "                    'rtu_004_oa_temp','rtu_004_ma_temp','zone_047_fan_spd','zone_047_hw_valve','rtu_004_ra_temp','rtu_004_sa_temp','zone_047_temp']].values\n",
-    "dataset = dataset.astype('float32')\n",
     "\n",
     "\n",
-    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
-    "dataset = scaler.fit_transform(dataset)\n",
-    "test_size = int(len(dataset)* 0.30)\n",
-    "test, train = dataset[0:test_size,:],dataset[test_size:len(dataset),:]\n",
+    "train,test = traindataset,testdataset\n",
     "\n",
     "def create_dataset(dataset,time_step):\n",
-    "    x1,x2,x3,x4,x5,x6,x7,x8,x9, Y = [],[],[],[],[],[],[],[],[],[]\n",
-    "\n",
-    "    for i in range(0,len(dataset)-time_step-1):\n",
+    "    x1,x2,x3,x4,x5,x6,x7,x8,x9,Y = [],[],[],[],[],[],[],[],[],[]\n",
+    "    for i in range(len(dataset)-time_step-1):\n",
     "        x1.append(dataset[i:(i+time_step), 0])\n",
     "        x2.append(dataset[i:(i+time_step), 1])\n",
     "        x3.append(dataset[i:(i+time_step), 2])\n",
@@ -759,213 +1036,843 @@
     "        x7.append(dataset[i:(i+time_step), 6])\n",
     "        x8.append(dataset[i:(i+time_step), 7])\n",
     "        x9.append(dataset[i:(i+time_step), 8])\n",
-    "        Y.append(dataset[i:(i+time_step), 8])\n",
+    "        Y.append([dataset[i + time_step, 7],dataset[i + time_step, 8]])\n",
     "    x1,x2,x3,x4,x5,x6,x7,x8,x9,Y =  np.array(x1),np.array(x2),np.array(x3), np.array(x4),np.array(x5),np.array(x6),np.array(x7),np.array(x8),np.array(x9),np.array(Y)\n",
-    "   \n",
-    "    # Y = np.reshape(Y,(len(Y),1))\n",
+    "    \n",
     "    return np.stack([x1,x2,x3,x4,x5,x6,x7,x8,x9],axis=2),Y\n",
     "\n",
     "\n",
     "\n",
     "\n",
-    "time_step = 60\n",
+    "time_step = 30\n",
     "X_train, y_train = create_dataset(train, time_step)\n",
     "X_test, y_test = create_dataset(test, time_step)\n",
     "\n",
     "\n",
     "model = Sequential()\n",
     "model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], X_train.shape[2])))\n",
-    "# model.add(LSTM(units=30))\n",
-    "# model.add(Dense(units=time_step))\n",
+    "model.add(LSTM(units=50, return_sequences=True))\n",
+    "model.add(LSTM(units=30))\n",
+    "model.add(Dense(units=2))\n",
     "\n",
     "model.compile(optimizer='adam', loss='mean_squared_error')\n",
     "\n",
-    "# model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=2, batch_size=64, verbose=1)\n",
+    "checkpoint_path = \"model_checkpoint4.keras\"\n",
+    "checkpoint_callback = ModelCheckpoint(filepath=checkpoint_path, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n",
+    "model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5, batch_size=64, verbose=1, callbacks=[checkpoint_callback])\n",
     "\n",
-    "train_predict = model.predict(X_train)\n",
-    "# test_predict = model.predict(X_test)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 244,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(779111, 60, 9)"
-      ]
-     },
-     "execution_count": 244,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_train.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mo"
+    "train_predict1 = model.predict(X_train)\n",
+    "test_predict1 = model.predict(X_test)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 241,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib qt\n",
-    "time = 10\n",
-    "mse = (y_test[time] - test_predict[0])**2\n",
-    "anomalies = np.where(mse > 0.0001)[0]\n",
-    "plt.plot(y_test[time], label='Original Data')\n",
-    "plt.plot(test_predict[time], label='predicted Data')\n",
-    "plt.scatter(anomalies,test_predict[time,anomalies], color='red', label='Anomalies')\n",
+    "#'rtu_004_ma_temp','rtu_004_sa_temp','rtu_004_fltrd_sa_flow_tn','rtu_004_sf_vfd_spd_fbk_tn'\n",
+    "var = 1\n",
+    "plt.plot(testdataset_df['date'][31:],y_test[:,var], label='Original Testing Data', color='blue')\n",
+    "plt.plot(testdataset_df['date'][31:],test_predict1[:,var], label='Predicted Testing Data', color='red',alpha=0.8)\n",
+    "# anomalies = np.where(abs(test_predict[:,var] - y_test[:,var]) > 0.38)[0]\n",
+    "# plt.scatter(anomalies,test_predict[anomalies,var], color='black',marker =\"o\",s=100 )\n",
+    "\n",
+    "\n",
+    "plt.title('Testing Data - Predicted vs Actual')\n",
+    "plt.xlabel('Time')\n",
+    "plt.ylabel('Value')\n",
     "plt.legend()\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 242,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_19\"</span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1mModel: \"sequential_19\"\u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ lstm_39 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)         │        <span style=\"color: #00af00; text-decoration-color: #00af00\">12,000</span> │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ lstm_40 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">9,720</span> │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_26 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,860</span> │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ lstm_39 (\u001b[38;5;33mLSTM\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m60\u001b[0m, \u001b[38;5;34m50\u001b[0m)         │        \u001b[38;5;34m12,000\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ lstm_40 (\u001b[38;5;33mLSTM\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m)             │         \u001b[38;5;34m9,720\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_26 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m60\u001b[0m)             │         \u001b[38;5;34m1,860\u001b[0m │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">70,742</span> (276.34 KB)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m70,742\u001b[0m (276.34 KB)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">23,580</span> (92.11 KB)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m23,580\u001b[0m (92.11 KB)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">47,162</span> (184.23 KB)\n",
-       "</pre>\n"
-      ],
       "text/plain": [
-       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m47,162\u001b[0m (184.23 KB)\n"
+       "0.022367444"
       ]
      },
+     "execution_count": 60,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "model.summary()"
+    "testdataset_inverse = scaler.inverse_transform(testdataset)\n",
+    "# # pred_inverse = scaler.inverse_transform(test_predict1)\n",
+    "np.mean((testdataset_inverse[31:,10]-test_predict1[:,1])**2)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 238,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([-0.00162351, -0.00127119, -0.00132501, -0.00043088, -0.00357354,\n",
-       "       -0.00349951, -0.00703096, -0.00679523,  0.00243503,  0.00075096,\n",
-       "        0.015773  ,  0.01411158,  0.01689583,  0.01593572,  0.00665802,\n",
-       "        0.0068891 , -0.001706  , -0.00310844, -0.00871575, -0.00967073,\n",
-       "       -0.00820547, -0.00687617,  0.00930512,  0.00670969,  0.00769156,\n",
-       "        0.01000088, -0.00052458,  0.00010484, -0.00573552, -0.00811213,\n",
-       "       -0.01016176, -0.01063424, -0.01580012, -0.01502603, -0.01243931,\n",
-       "       -0.01488668,  0.00733459,  0.00564003,  0.01374102,  0.01534522,\n",
-       "        0.00324941,  0.00375515, -0.0078848 , -0.00780392, -0.01223874,\n",
-       "       -0.01329106, -0.00772917, -0.00823385,  0.01035273,  0.01039612,\n",
-       "        0.01731664,  0.01493615,  0.00356281,  0.00522107, -0.00680918,\n",
-       "       -0.00461727, -0.00997645, -0.01072395, -0.00542653, -0.00710839],\n",
-       "      dtype=float32)"
+       "95.56315"
       ]
      },
-     "execution_count": 238,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "test_predict[0]-y_test[0]"
+    "(testdataset_inverse[31:,10]/test_predict1[:,1]).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/15\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:205: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(**kwargs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m3219/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.0176\n",
+      "Epoch 1: val_loss improved from inf to 0.02659, saving model to model_checkpoint2.keras\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m100s\u001b[0m 30ms/step - loss: 0.0176 - val_loss: 0.0266\n",
+      "Epoch 2/15\n",
+      "\u001b[1m3218/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.0024\n",
+      "Epoch 2: val_loss did not improve from 0.02659\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 30ms/step - loss: 0.0024 - val_loss: 0.0432\n",
+      "Epoch 3/15\n",
+      "\u001b[1m3219/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.0012\n",
+      "Epoch 3: val_loss did not improve from 0.02659\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m98s\u001b[0m 30ms/step - loss: 0.0012 - val_loss: 0.0395\n",
+      "Epoch 4/15\n",
+      "\u001b[1m3218/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 7.0729e-04\n",
+      "Epoch 4: val_loss did not improve from 0.02659\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m95s\u001b[0m 29ms/step - loss: 7.0723e-04 - val_loss: 0.0406\n",
+      "Epoch 5/15\n",
+      "\u001b[1m3218/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 5.3000e-04\n",
+      "Epoch 5: val_loss did not improve from 0.02659\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m95s\u001b[0m 30ms/step - loss: 5.2998e-04 - val_loss: 0.0403\n",
+      "Epoch 6/15\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 4.4223e-04\n",
+      "Epoch 6: val_loss did not improve from 0.02659\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━���━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m96s\u001b[0m 30ms/step - loss: 4.4222e-04 - val_loss: 0.0396\n",
+      "Epoch 7/15\n",
+      "\u001b[1m3219/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 3.8783e-04\n",
+      "Epoch 7: val_loss did not improve from 0.02659\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m140s\u001b[0m 29ms/step - loss: 3.8782e-04 - val_loss: 0.0405\n",
+      "Epoch 8/15\n",
+      "\u001b[1m3219/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 3.5622e-04\n",
+      "Epoch 8: val_loss did not improve from 0.02659\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m95s\u001b[0m 30ms/step - loss: 3.5621e-04 - val_loss: 0.0430\n",
+      "Epoch 9/15\n",
+      "\u001b[1m3219/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 3.2985e-04\n",
+      "Epoch 9: val_loss did not improve from 0.02659\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m95s\u001b[0m 30ms/step - loss: 3.2985e-04 - val_loss: 0.0428\n",
+      "Epoch 10/15\n",
+      "\u001b[1m3218/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 2.8355e-04\n",
+      "Epoch 10: val_loss did not improve from 0.02659\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m99s\u001b[0m 31ms/step - loss: 2.8354e-04 - val_loss: 0.0457\n",
+      "Epoch 11/15\n",
+      "\u001b[1m3218/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 2.3356e-04\n",
+      "Epoch 11: val_loss did not improve from 0.02659\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m100s\u001b[0m 31ms/step - loss: 2.3355e-04 - val_loss: 0.0504\n",
+      "Epoch 12/15\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 2.1286e-04\n",
+      "Epoch 12: val_loss did not improve from 0.02659\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m99s\u001b[0m 31ms/step - loss: 2.1286e-04 - val_loss: 0.0524\n",
+      "Epoch 13/15\n",
+      "\u001b[1m3218/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 1.9624e-04\n",
+      "Epoch 13: val_loss did not improve from 0.02659\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m101s\u001b[0m 31ms/step - loss: 1.9623e-04 - val_loss: 0.0522\n",
+      "Epoch 14/15\n",
+      "\u001b[1m3219/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 1.8412e-04\n",
+      "Epoch 14: val_loss did not improve from 0.02659\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m100s\u001b[0m 31ms/step - loss: 1.8412e-04 - val_loss: 0.0501\n",
+      "Epoch 15/15\n",
+      "\u001b[1m3218/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 1.7077e-04\n",
+      "Epoch 15: val_loss did not improve from 0.02659\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m101s\u001b[0m 31ms/step - loss: 1.7077e-04 - val_loss: 0.0497\n",
+      "\u001b[1m6440/6440\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 5ms/step\n",
+      "\u001b[1m9900/9900\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m49s\u001b[0m 5ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.layers import Reshape\n",
+    "traindataset = traindataset.astype('float32')\n",
+    "testdataset = testdataset.astype('float32')\n",
+    "\n",
+    "\n",
+    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "traindataset = scaler.fit_transform(traindataset)\n",
+    "testdataset = scaler.transform(testdataset)\n",
+    "\n",
+    "train,test = traindataset,testdataset\n",
+    "\n",
+    "def create_dataset(dataset,time_step):\n",
+    "    x1,x2,x3,x4,x5,x6,x7,x8,x9,Y = [],[],[],[],[],[],[],[],[],[]\n",
+    "    for i in range(len(dataset)-time_step-1):\n",
+    "        x1.append(dataset[i:(i+time_step), 0])\n",
+    "        x2.append(dataset[i:(i+time_step), 1])\n",
+    "        x3.append(dataset[i:(i+time_step), 2])\n",
+    "        x4.append(dataset[i:(i+time_step), 3])\n",
+    "        x5.append(dataset[i:(i+time_step), 4])\n",
+    "        x6.append(dataset[i:(i+time_step), 5])\n",
+    "        x7.append(dataset[i:(i+time_step), 6])\n",
+    "        x8.append(dataset[i:(i+time_step), 7])\n",
+    "        x9.append(dataset[i:(i+time_step), 8])\n",
+    "        Y.append([dataset[i:(i+time_step), 7],dataset[i:(i+time_step), 8]])\n",
+    "    x1,x2,x3,x4,x5,x6,x7,x8,x9,Y =  np.array(x1),np.array(x2),np.array(x3), np.array(x4),np.array(x5),np.array(x6),np.array(x7),np.array(x8),np.array(x9),np.array(Y)\n",
+    "    return np.stack([x1,x2,x3,x4,x5,x6,x7,x8,x9],axis=2),Y\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "time_step = 30\n",
+    "X_train, y_train = create_dataset(train, time_step)\n",
+    "X_test, y_test = create_dataset(test, time_step)\n",
+    "\n",
+    "\n",
+    "model = Sequential()\n",
+    "model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], X_train.shape[2])))\n",
+    "model.add(LSTM(units=50, return_sequences=True))\n",
+    "model.add(LSTM(units=50, return_sequences=True))\n",
+    "model.add(LSTM(units=25))\n",
+    "model.add(Dense(units=30*2))\n",
+    "model.add(Reshape((2, 30)))\n",
+    "\n",
+    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "\n",
+    "checkpoint_path = \"model_checkpoint2.keras\"\n",
+    "checkpoint_callback = ModelCheckpoint(filepath=checkpoint_path, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n",
+    "model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=15, batch_size=64, verbose=1, callbacks=[checkpoint_callback])\n",
+    "\n",
+    "train_predict1 = model.predict(X_train)\n",
+    "test_predict1 = model.predict(X_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 239,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/6\n",
+      "\u001b[1m3218/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.6263e-04\n",
+      "Epoch 1: val_loss improved from inf to 0.01449, saving model to model_checkpoint1.keras\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 17ms/step - loss: 2.6263e-04 - val_loss: 0.0145\n",
+      "Epoch 2/6\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.4574e-04\n",
+      "Epoch 2: val_loss improved from 0.01449 to 0.01404, saving model to model_checkpoint1.keras\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 17ms/step - loss: 2.4575e-04 - val_loss: 0.0140\n",
+      "Epoch 3/6\n",
+      "\u001b[1m3218/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.4207e-04\n",
+      "Epoch 3: val_loss improved from 0.01404 to 0.01223, saving model to model_checkpoint1.keras\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 18ms/step - loss: 2.4206e-04 - val_loss: 0.0122\n",
+      "Epoch 4/6\n",
+      "\u001b[1m3219/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.1951e-04\n",
+      "Epoch 4: val_loss did not improve from 0.01223\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 18ms/step - loss: 2.1951e-04 - val_loss: 0.0128\n",
+      "Epoch 5/6\n",
+      "\u001b[1m3217/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.0860e-04\n",
+      "Epoch 5: val_loss did not improve from 0.01223\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 19ms/step - loss: 2.0860e-04 - val_loss: 0.0137\n",
+      "Epoch 6/6\n",
+      "\u001b[1m3219/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.9718e-04\n",
+      "Epoch 6: val_loss did not improve from 0.01223\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 17ms/step - loss: 1.9718e-04 - val_loss: 0.0145\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x1e018769c90>"
+      ]
+     },
+     "execution_count": 239,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "checkpoint_callback = ModelCheckpoint(filepath=checkpoint_path, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n",
+    "model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=6, batch_size=64, verbose=1, callbacks=[checkpoint_callback])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\ipykernel\\eventloops.py:145: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  el.exec() if hasattr(el, \"exec\") else el.exec_()\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib qt\n",
+    "#'rtu_004_ma_temp','rtu_004_sa_temp','rtu_004_fltrd_sa_flow_tn','rtu_004_sf_vfd_spd_fbk_tn'\n",
+    "var = 1\n",
+    "plt.plot(testdataset_df['date'][31:],y_test[:,var], label='Original Testing Data', color='blue')\n",
+    "plt.plot(testdataset_df['date'][31:],test_predict1[:,var], label='Predicted Testing Data', color='red',alpha=0.8)\n",
+    "# anomalies = np.where(abs(test_predict[:,var] - y_test[:,var]) > 0.38)[0]\n",
+    "# plt.scatter(anomalies,test_predict[anomalies,var], color='black',marker =\"o\",s=100 )\n",
+    "\n",
+    "\n",
+    "plt.title('Testing Data - Predicted vs Actual')\n",
+    "plt.xlabel('Time')\n",
+    "plt.ylabel('Value')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 235,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/6\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:205: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(**kwargs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m3218/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0107\n",
+      "Epoch 1: val_loss improved from inf to 0.08094, saving model to model_checkpoint1.keras\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 17ms/step - loss: 0.0107 - val_loss: 0.0809\n",
+      "Epoch 2/6\n",
+      "\u001b[1m1187/3220\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m26s\u001b[0m 13ms/step - loss: 0.0012"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[235], line 44\u001b[0m\n\u001b[0;32m     41\u001b[0m checkpoint_callback \u001b[38;5;241m=\u001b[39m ModelCheckpoint(filepath\u001b[38;5;241m=\u001b[39mcheckpoint_path, monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mval_loss\u001b[39m\u001b[38;5;124m'\u001b[39m, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, save_best_only\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m     43\u001b[0m \u001b[38;5;66;03m# Train the model with the checkpoint callback\u001b[39;00m\n\u001b[1;32m---> 44\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_test\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m6\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m64\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mcheckpoint_callback\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     46\u001b[0m train_predict2 \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mpredict(X_train)\n\u001b[0;32m     47\u001b[0m test_predict2 \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mpredict(X_test)\n",
+      "File \u001b[1;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:118\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    116\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    117\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 118\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    119\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    120\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
+      "File \u001b[1;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py:323\u001b[0m, in \u001b[0;36mTensorFlowTrainer.fit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001b[0m\n\u001b[0;32m    321\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step, iterator \u001b[38;5;129;01min\u001b[39;00m epoch_iterator\u001b[38;5;241m.\u001b[39menumerate_epoch():\n\u001b[0;32m    322\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[1;32m--> 323\u001b[0m     logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    324\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_end(\n\u001b[0;32m    325\u001b[0m         step, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pythonify_logs(logs)\n\u001b[0;32m    326\u001b[0m     )\n\u001b[0;32m    327\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstop_training:\n",
+      "File \u001b[1;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
+      "File \u001b[1;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:833\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    830\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    832\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[1;32m--> 833\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    835\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[0;32m    836\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
+      "File \u001b[1;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:878\u001b[0m, in \u001b[0;36mFunction._call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    875\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[0;32m    876\u001b[0m \u001b[38;5;66;03m# In this case we have not created variables on the first call. So we can\u001b[39;00m\n\u001b[0;32m    877\u001b[0m \u001b[38;5;66;03m# run the first trace but we should fail if variables are created.\u001b[39;00m\n\u001b[1;32m--> 878\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mtracing_compilation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    879\u001b[0m \u001b[43m    \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_variable_creation_config\u001b[49m\n\u001b[0;32m    880\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    881\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_created_variables:\n\u001b[0;32m    882\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating variables on a non-first call to a function\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    883\u001b[0m                    \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m decorated with tf.function.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\tracing_compilation.py:139\u001b[0m, in \u001b[0;36mcall_function\u001b[1;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[0;32m    137\u001b[0m bound_args \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mbind(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    138\u001b[0m flat_inputs \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39munpack_inputs(bound_args)\n\u001b[1;32m--> 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# pylint: disable=protected-access\u001b[39;49;00m\n\u001b[0;32m    140\u001b[0m \u001b[43m    \u001b[49m\u001b[43mflat_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\n\u001b[0;32m    141\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\concrete_function.py:1322\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[1;34m(self, tensor_inputs, captured_inputs)\u001b[0m\n\u001b[0;32m   1318\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[0;32m   1319\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[0;32m   1320\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[0;32m   1321\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[1;32m-> 1322\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_preflattened\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1323\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[0;32m   1324\u001b[0m     args,\n\u001b[0;32m   1325\u001b[0m     possible_gradient_type,\n\u001b[0;32m   1326\u001b[0m     executing_eagerly)\n\u001b[0;32m   1327\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
+      "File \u001b[1;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\atomic_function.py:216\u001b[0m, in \u001b[0;36mAtomicFunction.call_preflattened\u001b[1;34m(self, args)\u001b[0m\n\u001b[0;32m    214\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcall_preflattened\u001b[39m(\u001b[38;5;28mself\u001b[39m, args: Sequence[core\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[0;32m    215\u001b[0m \u001b[38;5;250m  \u001b[39m\u001b[38;5;124;03m\"\"\"Calls with flattened tensor inputs and returns the structured output.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 216\u001b[0m   flat_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    217\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mpack_output(flat_outputs)\n",
+      "File \u001b[1;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\atomic_function.py:251\u001b[0m, in \u001b[0;36mAtomicFunction.call_flat\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    249\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[0;32m    250\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[1;32m--> 251\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_bound_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    252\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    253\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflat_outputs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    255\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    256\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    257\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\n\u001b[0;32m    258\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    259\u001b[0m         \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[0;32m    260\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mfunction_call_options\u001b[38;5;241m.\u001b[39mas_attrs(),\n\u001b[0;32m    261\u001b[0m     )\n",
+      "File \u001b[1;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\context.py:1500\u001b[0m, in \u001b[0;36mContext.call_function\u001b[1;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[0;32m   1498\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[0;32m   1499\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1500\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1501\u001b[0m \u001b[43m      \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1502\u001b[0m \u001b[43m      \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1503\u001b[0m \u001b[43m      \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtensor_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1504\u001b[0m \u001b[43m      \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1505\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1506\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1507\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1508\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[0;32m   1509\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m   1510\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1514\u001b[0m       cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[0;32m   1515\u001b[0m   )\n",
+      "File \u001b[1;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\tensorflow\\python\\eager\\execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     54\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "traindataset = traindataset.astype('float32')\n",
+    "testdataset = testdataset.astype('float32')\n",
+    "\n",
+    "\n",
+    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "traindataset = scaler.fit_transform(traindataset)\n",
+    "testdataset = scaler.transform(testdataset)\n",
+    "\n",
+    "train,test = traindataset,testdataset\n",
+    "\n",
+    "def create_dataset(dataset,time_step):\n",
+    "    x1,x2,x3,x4,x5,x6,x7,Y = [],[],[],[],[],[],[],[]\n",
+    "    for i in range(len(dataset)-time_step-1):\n",
+    "        x1.append(dataset[i:(i+time_step), 0])\n",
+    "        x2.append(dataset[i:(i+time_step), 1])\n",
+    "        x3.append(dataset[i:(i+time_step), 2])\n",
+    "        x4.append(dataset[i:(i+time_step), 3])\n",
+    "        x5.append(dataset[i:(i+time_step), 4])\n",
+    "        x6.append(dataset[i:(i+time_step), 5])\n",
+    "        x7.append(dataset[i:(i+time_step), 6])\n",
+    "        Y.append([dataset[i + time_step, 7],dataset[i + time_step, 8],dataset[i + time_step, 9],dataset[i + time_step, 10]])\n",
+    "    x1,x2,x3,x4,x5,x6,x7,Y =  np.array(x1),np.array(x2),np.array(x3), np.array(x4),np.array(x5),np.array(x6),np.array(x7),np.array(Y)\n",
+    "    return np.stack([x1,x2,x3,x4,x5,x6,x7],axis=2),Y\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "time_step = 30\n",
+    "X_train, y_train = create_dataset(train, time_step)\n",
+    "X_test, y_test = create_dataset(test, time_step)\n",
+    "\n",
+    "\n",
+    "model = Sequential()\n",
+    "model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], X_train.shape[2])))\n",
+    "model.add(LSTM(units=50))\n",
+    "model.add(Dense(units=4))\n",
+    "\n",
+    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "\n",
+    "model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=64, verbose=1)\n",
+    "\n",
+    "train_predict2 = model.predict(X_train)\n",
+    "test_predict2 = model.predict(X_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 224,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib qt\n",
+    "#'rtu_004_ma_temp','rtu_004_sa_temp','rtu_004_fltrd_sa_flow_tn','rtu_004_sf_vfd_spd_fbk_tn'\n",
+    "var = 1\n",
+    "plt.plot(testdataset_df['date'][31:],y_test[:,var], label='Original Testing Data', color='blue')\n",
+    "plt.plot(testdataset_df['date'][31:],test_predict2[:,var], label='Predicted Testing Data', color='red',alpha=0.8)\n",
+    "# anomalies = np.where(abs(test_predict[:,var] - y_test[:,var]) > 0.38)[0]\n",
+    "# plt.scatter(anomalies,test_predict[anomalies,var], color='black',marker =\"o\",s=100 )\n",
+    "\n",
+    "\n",
+    "plt.title('Testing Data - Predicted vs Actual')\n",
+    "plt.xlabel('Time')\n",
+    "plt.ylabel('Value')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "testdataset_df = final_df[(final_df.date.dt.date >date(2019, 7, 8)) & (final_df.date.dt.date <date(2019, 11, 8))]\n",
+    "\n",
+    "traindataset_df = final_df[ (final_df.date.dt.date >date(2019, 11, 8))]\n",
+    "\n",
+    "testdataset = testdataset_df[['rtu_004_oa_temp','rtu_004_ra_temp','hp_hws_temp','rtu_004_oa_flow_tn','rtu_004_oadmpr_pct',\n",
+    "                    'rtu_004_sat_sp_tn','rtu_004_rf_vfd_spd_fbk_tn','rtu_004_ma_temp','rtu_004_sa_temp','rtu_004_fltrd_sa_flow_tn',\n",
+    "                    'rtu_004_sf_vfd_spd_fbk_tn']].values\n",
+    "\n",
+    "\n",
+    "traindataset = traindataset_df[['rtu_004_oa_temp','rtu_004_ra_temp','hp_hws_temp','rtu_004_oa_flow_tn','rtu_004_oadmpr_pct',\n",
+    "                    'rtu_004_sat_sp_tn','rtu_004_rf_vfd_spd_fbk_tn','rtu_004_ma_temp','rtu_004_sa_temp','rtu_004_fltrd_sa_flow_tn',\n",
+    "                    'rtu_004_sf_vfd_spd_fbk_tn']].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 217,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/3\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:205: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(**kwargs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m3219/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 0.0085\n",
+      "Epoch 1: val_loss improved from inf to 0.02416, saving model to model_checkpoint.keras\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m128s\u001b[0m 39ms/step - loss: 0.0085 - val_loss: 0.0242\n",
+      "Epoch 2/3\n",
+      "\u001b[1m3219/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 0.0014\n",
+      "Epoch 2: val_loss improved from 0.02416 to 0.01842, saving model to model_checkpoint.keras\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m125s\u001b[0m 39ms/step - loss: 0.0014 - val_loss: 0.0184\n",
+      "Epoch 3/3\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 6.4067e-04\n",
+      "Epoch 3: val_loss improved from 0.01842 to 0.01474, saving model to model_checkpoint.keras\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m126s\u001b[0m 39ms/step - loss: 6.4065e-04 - val_loss: 0.0147\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x1dd821c93d0>"
+      ]
+     },
+     "execution_count": 217,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "traindataset = traindataset.astype('float32')\n",
+    "testdataset = testdataset.astype('float32')\n",
+    "\n",
+    "\n",
+    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "traindataset = scaler.fit_transform(traindataset)\n",
+    "testdataset = scaler.transform(testdataset)\n",
+    "\n",
+    "train,test = traindataset,testdataset\n",
+    "\n",
+    "def create_dataset(dataset,time_step):\n",
+    "    x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,Y = [],[],[],[],[],[],[],[],[],[],[],[]\n",
+    "    for i in range(len(dataset)-time_step-1):\n",
+    "        x1.append(dataset[i:(i+time_step), 0])\n",
+    "        x2.append(dataset[i:(i+time_step), 1])\n",
+    "        x3.append(dataset[i:(i+time_step), 2])\n",
+    "        x4.append(dataset[i:(i+time_step), 3])\n",
+    "        x5.append(dataset[i:(i+time_step), 4])\n",
+    "        x6.append(dataset[i:(i+time_step), 5])\n",
+    "        x7.append(dataset[i:(i+time_step), 6])\n",
+    "        x8.append(dataset[i:(i+time_step), 7])\n",
+    "        x9.append(dataset[i:(i+time_step), 8])\n",
+    "        x10.append(dataset[i:(i+time_step), 9])\n",
+    "        x11.append(dataset[i:(i+time_step), 10])\n",
+    "        Y.append([dataset[i + time_step, 7],dataset[i + time_step, 8],dataset[i + time_step, 9],dataset[i + time_step, 10]])\n",
+    "    x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,Y =  np.array(x1),np.array(x2),np.array(x3), np.array(x4),np.array(x5),np.array(x6),np.array(x7),np.array(x8),np.array(x9),np.array(x10),np.array(x11),np.array(Y)\n",
+    "    return np.stack([x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11],axis=2),Y\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "time_step = 30\n",
+    "X_train, y_train = create_dataset(train, time_step)\n",
+    "X_test, y_test = create_dataset(test, time_step)\n",
+    "\n",
+    "\n",
+    "model = Sequential()\n",
+    "model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], X_train.shape[2])))\n",
+    "model.add(LSTM(units=50,return_sequences=True))\n",
+    "model.add(LSTM(units=50,return_sequences=True))\n",
+    "model.add(LSTM(units=25))\n",
+    "model.add(Dense(units=4))\n",
+    "\n",
+    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "\n",
+    "\n",
+    "checkpoint_path = \"model_checkpoint.keras\"\n",
+    "checkpoint_callback = ModelCheckpoint(filepath=checkpoint_path, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n",
+    "\n",
+    "# Train the model with the checkpoint callback\n",
+    "model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=3, batch_size=64, verbose=1, callbacks=[checkpoint_callback])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 219,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 2.9152e-04\n",
+      "Epoch 1: val_loss improved from 0.01216 to 0.01211, saving model to model_checkpoint.keras\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m129s\u001b[0m 40ms/step - loss: 2.9152e-04 - val_loss: 0.0121\n",
+      "Epoch 2/5\n",
+      "\u001b[1m3219/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 2.8792e-04\n",
+      "Epoch 2: val_loss did not improve from 0.01211\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m132s\u001b[0m 41ms/step - loss: 2.8793e-04 - val_loss: 0.0124\n",
+      "Epoch 3/5\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 2.7926e-04\n",
+      "Epoch 3: val_loss did not improve from 0.01211\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m131s\u001b[0m 41ms/step - loss: 2.7926e-04 - val_loss: 0.0127\n",
+      "Epoch 4/5\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 2.7536e-04\n",
+      "Epoch 4: val_loss did not improve from 0.01211\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m125s\u001b[0m 39ms/step - loss: 2.7536e-04 - val_loss: 0.0144\n",
+      "Epoch 5/5\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 2.6916e-04\n",
+      "Epoch 5: val_loss did not improve from 0.01211\n",
+      "\u001b[1m3220/3220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m96s\u001b[0m 30ms/step - loss: 2.6916e-04 - val_loss: 0.0154\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x1dd85e7a8d0>"
+      ]
+     },
+     "execution_count": 219,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.load_weights(checkpoint_path)\n",
+    "model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5, batch_size=64, verbose=1, callbacks=[checkpoint_callback])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_predict2 = model.predict(X_train)\n",
+    "test_predict2 = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib qt\n",
+    "#'rtu_004_ma_temp','rtu_004_sa_temp','rtu_004_fltrd_sa_flow_tn','rtu_004_sf_vfd_spd_fbk_tn'\n",
+    "\n",
+    "var = 1\n",
+    "plt.plot(testdataset_df['date'][31:],y_test[:,var], label='Original Testing Data', color='blue')\n",
+    "plt.plot(testdataset_df['date'][31:],test_predict2[:,var], label='Predicted Testing Data', color='red',alpha=0.8)\n",
+    "anomalies = np.where(abs(test_predict2[:,var] - y_test[:,var]) > 0.4)[0]\n",
+    "plt.scatter(testdataset_df.iloc[anomalies+31]['date'],test_predict2[anomalies,var], color='black',marker =\"o\",s=100 )\n",
+    "\n",
+    "\n",
+    "plt.title('Testing Data - Predicted vs Actual')\n",
+    "plt.xlabel('Time')\n",
+    "plt.ylabel('Temperature')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "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>date</th>\n",
+       "      <th>zone_047_hw_valve</th>\n",
+       "      <th>rtu_004_sat_sp_tn</th>\n",
+       "      <th>zone_047_temp</th>\n",
+       "      <th>zone_047_fan_spd</th>\n",
+       "      <th>rtu_004_fltrd_sa_flow_tn</th>\n",
+       "      <th>rtu_004_sa_temp</th>\n",
+       "      <th>rtu_004_pa_static_stpt_tn</th>\n",
+       "      <th>rtu_004_oa_flow_tn</th>\n",
+       "      <th>rtu_004_oadmpr_pct</th>\n",
+       "      <th>...</th>\n",
+       "      <th>zone_047_heating_sp</th>\n",
+       "      <th>Unnamed: 47_y</th>\n",
+       "      <th>hvac_S</th>\n",
+       "      <th>hp_hws_temp</th>\n",
+       "      <th>aru_001_cwr_temp</th>\n",
+       "      <th>aru_001_cws_fr_gpm</th>\n",
+       "      <th>aru_001_cws_temp</th>\n",
+       "      <th>aru_001_hwr_temp</th>\n",
+       "      <th>aru_001_hws_fr_gpm</th>\n",
+       "      <th>aru_001_hws_temp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>555845</th>\n",
+       "      <td>2019-04-02 00:00:00</td>\n",
+       "      <td>62.3</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>11140.524</td>\n",
+       "      <td>67.3</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>9763.184355</td>\n",
+       "      <td>40.8</td>\n",
+       "      <td>...</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.255</td>\n",
+       "      <td>120.7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>555846</th>\n",
+       "      <td>2019-04-02 00:01:00</td>\n",
+       "      <td>62.3</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>11140.524</td>\n",
+       "      <td>68.6</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>9774.455363</td>\n",
+       "      <td>40.8</td>\n",
+       "      <td>...</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.255</td>\n",
+       "      <td>120.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 30 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      date  zone_047_hw_valve  rtu_004_sat_sp_tn  \\\n",
+       "555845 2019-04-02 00:00:00               62.3               68.0   \n",
+       "555846 2019-04-02 00:01:00               62.3               68.0   \n",
+       "\n",
+       "        zone_047_temp  zone_047_fan_spd  rtu_004_fltrd_sa_flow_tn  \\\n",
+       "555845           72.0              20.0                 11140.524   \n",
+       "555846           72.0              20.0                 11140.524   \n",
+       "\n",
+       "        rtu_004_sa_temp  rtu_004_pa_static_stpt_tn  rtu_004_oa_flow_tn  \\\n",
+       "555845             67.3                       0.06         9763.184355   \n",
+       "555846             68.6                       0.06         9774.455363   \n",
+       "\n",
+       "        rtu_004_oadmpr_pct  ...  zone_047_heating_sp  Unnamed: 47_y  hvac_S  \\\n",
+       "555845                40.8  ...                 72.0            NaN   8.255   \n",
+       "555846                40.8  ...                 72.0            NaN   8.255   \n",
+       "\n",
+       "        hp_hws_temp  aru_001_cwr_temp  aru_001_cws_fr_gpm  aru_001_cws_temp  \\\n",
+       "555845        120.7               NaN                 NaN               NaN   \n",
+       "555846        120.4               NaN                 NaN               NaN   \n",
+       "\n",
+       "        aru_001_hwr_temp  aru_001_hws_fr_gpm  aru_001_hws_temp  \n",
+       "555845               NaN                 NaN               NaN  \n",
+       "555846               NaN                 NaN               NaN  \n",
+       "\n",
+       "[2 rows x 30 columns]"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "testdataset_df.iloc[[0,1]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.8437156, 0.8396924], dtype=float32)"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_predict2[[0,1],var]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'X_train' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mX_train\u001b[49m\u001b[38;5;241m.\u001b[39mshape\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'X_train' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "X_train.shape"
    ]
   },
   {