diff --git a/model.ipynb b/model.ipynb
deleted file mode 100644
index e69de29..0000000
diff --git a/model_base.ipynb b/model_base.ipynb
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index 0000000..189305a
--- /dev/null
+++ b/model_base.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import h5py\n",
+    "import numpy as np\n",
+    "from datetime import datetime\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import keras\n",
+    "from keras import layers\n",
+    "from keras import models\n",
+    "\n",
+    "spatio_feature = None\n",
+    "\n",
+    "input_shape = (5,1)\n",
+    "\n",
+    "left_inputs = layers.Input(shape = input_shape)\n",
+    "\n",
+    "x = models.layers.LSTM(50, activation = \"tanh\")(left_inputs)\n",
+    "\n",
+    "x = layers.Dense(2*16*8, activation = \"linear\")(x)\n",
+    "\n",
+    "output = layers.Reshape((2,16,8))(x)\n",
+    "\n",
+    "model = models.Model([left_inputs],outputs = output)\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py38",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/model_v1.ipynb b/model_v1.ipynb
new file mode 100644
index 0000000..db6c599
--- /dev/null
+++ b/model_v1.ipynb
@@ -0,0 +1,279 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import h5py\n",
+    "import numpy as np\n",
+    "from datetime import datetime\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with h5py.File('./data/TrainingData.h5', 'r') as f:\n",
+    "    # Access the trip dataset and their corresponding timestamps\n",
+    "    traffic_data = f['trip'][()]\n",
+    "    dates = f['timeslot'][()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "formatted_dates = []\n",
+    "\n",
+    "for date_string in dates:\n",
+    "    formatted_date = datetime.strptime(date_string.decode(), '%Y%m%d%H%M')\n",
+    "\n",
+    "    year = formatted_date.year\n",
+    "    month = formatted_date.month\n",
+    "    day = formatted_date.day\n",
+    "    hour = formatted_date.hour\n",
+    "    minute = formatted_date.minute\n",
+    "\n",
+    "    formatted_dates.append(np.array([year, month, day, hour, minute]))\n",
+    "\n",
+    "formatted_dates = np.array(formatted_dates).reshape(1488, 5, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_size = 240\n",
+    "\n",
+    "train_traffic_data = traffic_data[:-test_size]\n",
+    "test_traffic_data = traffic_data[-test_size:]\n",
+    "\n",
+    "train_formatted_dates = formatted_dates[:-test_size]\n",
+    "test_formatted_dates = formatted_dates[-test_size:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test = train_formatted_dates, test_formatted_dates\n",
+    "y_train, y_test = train_traffic_data, test_traffic_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import keras\n",
+    "from keras import layers\n",
+    "from keras import models\n",
+    "\n",
+    "spatio_feature = None\n",
+    "\n",
+    "input_shape = (5,1)\n",
+    "\n",
+    "left_inputs = layers.Input(shape = input_shape)\n",
+    "\n",
+    "x = layers.LSTM(50, activation = \"tanh\")(left_inputs)\n",
+    "\n",
+    "x = layers.Dense(2*16*8, activation = \"linear\")(x)\n",
+    "\n",
+    "output = layers.Reshape((2,16,8))(x)\n",
+    "\n",
+    "model = models.Model([left_inputs],outputs = output)\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/50\n",
+      "39/39 [==============================] - 1s 9ms/step - loss: 212.9115 - val_loss: 144.5777\n",
+      "Epoch 2/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 183.6068 - val_loss: 118.8507\n",
+      "Epoch 3/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 158.2998 - val_loss: 105.8254\n",
+      "Epoch 4/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 143.8591 - val_loss: 98.0465\n",
+      "Epoch 5/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 134.1396 - val_loss: 93.0498\n",
+      "Epoch 6/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 127.2775 - val_loss: 89.7777\n",
+      "Epoch 7/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 122.2552 - val_loss: 87.7291\n",
+      "Epoch 8/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 118.6310 - val_loss: 86.4313\n",
+      "Epoch 9/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 115.9718 - val_loss: 85.7162\n",
+      "Epoch 10/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 114.0570 - val_loss: 85.3153\n",
+      "Epoch 11/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 112.6366 - val_loss: 85.1707\n",
+      "Epoch 12/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 111.6274 - val_loss: 85.1579\n",
+      "Epoch 13/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 110.8910 - val_loss: 85.2295\n",
+      "Epoch 14/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 110.3705 - val_loss: 85.3980\n",
+      "Epoch 15/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 109.9801 - val_loss: 85.5780\n",
+      "Epoch 16/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 109.7158 - val_loss: 85.8827\n",
+      "Epoch 17/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 109.5257 - val_loss: 86.0089\n",
+      "Epoch 18/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 109.4247 - val_loss: 86.1505\n",
+      "Epoch 19/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 109.0258 - val_loss: 85.7924\n",
+      "Epoch 20/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 108.8352 - val_loss: 85.9186\n",
+      "Epoch 21/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 108.5763 - val_loss: 85.5510\n",
+      "Epoch 22/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 108.1767 - val_loss: 85.3380\n",
+      "Epoch 23/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 107.4323 - val_loss: 84.9938\n",
+      "Epoch 24/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 106.1936 - val_loss: 82.6167\n",
+      "Epoch 25/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 104.6931 - val_loss: 81.7814\n",
+      "Epoch 26/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 102.9895 - val_loss: 79.9620\n",
+      "Epoch 27/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 101.5745 - val_loss: 78.4792\n",
+      "Epoch 28/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 98.5761 - val_loss: 76.5478\n",
+      "Epoch 29/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 94.6209 - val_loss: 72.0995\n",
+      "Epoch 30/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 90.4683 - val_loss: 70.5429\n",
+      "Epoch 31/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 86.6393 - val_loss: 67.5324\n",
+      "Epoch 32/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 83.6719 - val_loss: 66.9005\n",
+      "Epoch 33/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 81.2981 - val_loss: 65.7497\n",
+      "Epoch 34/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 79.8582 - val_loss: 65.4873\n",
+      "Epoch 35/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 78.4644 - val_loss: 64.4355\n",
+      "Epoch 36/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 77.4534 - val_loss: 65.0040\n",
+      "Epoch 37/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 76.9951 - val_loss: 65.3200\n",
+      "Epoch 38/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 76.4038 - val_loss: 64.2204\n",
+      "Epoch 39/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 75.8992 - val_loss: 64.0133\n",
+      "Epoch 40/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 75.4885 - val_loss: 64.4360\n",
+      "Epoch 41/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 75.2467 - val_loss: 64.6135\n",
+      "Epoch 42/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 74.6445 - val_loss: 64.3743\n",
+      "Epoch 43/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 74.2044 - val_loss: 63.9837\n",
+      "Epoch 44/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 73.8331 - val_loss: 64.3629\n",
+      "Epoch 45/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 73.4110 - val_loss: 63.6357\n",
+      "Epoch 46/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 72.9355 - val_loss: 63.7812\n",
+      "Epoch 47/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 72.2754 - val_loss: 63.3691\n",
+      "Epoch 48/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 71.8866 - val_loss: 64.9375\n",
+      "Epoch 49/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 71.0808 - val_loss: 63.7018\n",
+      "Epoch 50/50\n",
+      "39/39 [==============================] - 0s 2ms/step - loss: 70.5971 - val_loss: 62.9802\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x1b1db2e4a90>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.compile(optimizer='adam', loss='mse')\n",
+    "model.fit(X_train, y_train, epochs=50, batch_size=32, validation_data=(X_test, y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8/8 [==============================] - 0s 1000us/step - loss: 62.9802\n",
+      "Root Mean Squared Error: 7.936009544951329\n"
+     ]
+    }
+   ],
+   "source": [
+    "mse = model.evaluate(X_test, y_test)\n",
+    "\n",
+    "# Show rmse to see how model performs on the test set\n",
+    "rmse = np.sqrt(mse)\n",
+    "print(f'Root Mean Squared Error: {rmse}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py38",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}