keras-for-modeling-linear-regression.ipynb

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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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   "source": [
    "import numpy as np\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Input, Activation\n",
    "from tensorflow.keras.datasets import boston_housing\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt"
   ]
  },
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   "id": "dc3d9925",
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   "source": [
    "SEED_VALUE = 42\n",
    "\n",
    "\n",
    "# to make training deterministic\n",
    "np.random.seed(SEED_VALUE)\n",
    "tf.random.set_seed(SEED_VALUE)"
   ]
  },
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   "id": "ed9900f5",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/boston_housing.npz\n",
      "\u001b[1m57026/57026\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n",
      "(404, 13)\n",
      "\n",
      "\n",
      "Input features: [  1.23247   0.        8.14      0.        0.538     6.142    91.7\n",
      "   3.9769    4.      307.       21.      396.9      18.72   ]\n",
      "\n",
      "Output target: 15.2\n"
     ]
    }
   ],
   "source": [
    "(X_train, y_train), (X_test, y_test) = boston_housing.load_data()\n",
    "\n",
    "print(X_train.shape)\n",
    "print(\"\\n\")\n",
    "print(f\"Input features: {X_train[0]}\\n\\nOutput target: {y_train[0]}\")"
   ]
  },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(404,)\n"
     ]
    }
   ],
   "source": [
    "# extract one target feature\n",
    "boston_features = {\n",
    "    \"Average Numbers of Rooms\": 5\n",
    "}\n",
    "\n",
    "X_train_1d = X_train[:, boston_features[\"Average Numbers of Rooms\"]]\n",
    "print(X_train_1d.shape)\n",
    "\n",
    "X_test_1d = X_test[:, boston_features[\"Average Numbers of Rooms\"]]"
   ]
  },
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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "\n",
    "plt.xlabel(\"Average Numbers of Rooms\")\n",
    "plt.ylabel(\"Median price [$K]\")\n",
    "plt.grid(\"on\")\n",
    "plt.scatter(X_train_1d[:], y_train, color=\"orange\", alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ceb4d84e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-19T20:06:53.027650Z",
     "iopub.status.busy": "2024-10-19T20:06:53.026723Z",
     "iopub.status.idle": "2024-10-19T20:06:53.133258Z",
     "shell.execute_reply": "2024-10-19T20:06:53.131991Z"
    },
    "papermill": {
     "duration": 0.120197,
     "end_time": "2024-10-19T20:06:53.135869",
     "exception": false,
     "start_time": "2024-10-19T20:06:53.015672",
     "status": "completed"
    },
    "tags": []
   },
   "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\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\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",
       "│ dense (<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\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">2</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",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m2\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\">2</span> (8.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2\u001b[0m (8.00 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\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> (8.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2\u001b[0m (8.00 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\"> 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"
    }
   ],
   "source": [
    "# define the Keras model\n",
    "model = Sequential()\n",
    "\n",
    "# with 1 neuron\n",
    "model.add(Input(shape=(1,)))\n",
    "model.add(Dense(units=1))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a060ee81",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-19T20:06:53.157414Z",
     "iopub.status.busy": "2024-10-19T20:06:53.156995Z",
     "iopub.status.idle": "2024-10-19T20:06:53.174414Z",
     "shell.execute_reply": "2024-10-19T20:06:53.172903Z"
    },
    "papermill": {
     "duration": 0.031621,
     "end_time": "2024-10-19T20:06:53.177850",
     "exception": false,
     "start_time": "2024-10-19T20:06:53.146229",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# compilation\n",
    "model.compile(optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.005), loss=\"mse\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2b39eb4f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-19T20:06:53.198563Z",
     "iopub.status.busy": "2024-10-19T20:06:53.198152Z",
     "iopub.status.idle": "2024-10-19T20:07:02.569391Z",
     "shell.execute_reply": "2024-10-19T20:07:02.567963Z"
    },
    "papermill": {
     "duration": 9.384603,
     "end_time": "2024-10-19T20:07:02.572292",
     "exception": false,
     "start_time": "2024-10-19T20:06:53.187689",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - loss: 245.6270 - val_loss: 326.3054\n",
      "Epoch 2/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 223.8670 - val_loss: 306.1152\n",
      "Epoch 3/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 207.5118 - val_loss: 287.4150\n",
      "Epoch 4/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 192.3265 - val_loss: 269.6430\n",
      "Epoch 5/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 177.9988 - val_loss: 252.7203\n",
      "Epoch 6/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 164.4828 - val_loss: 236.6340\n",
      "Epoch 7/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 151.7694 - val_loss: 221.3807\n",
      "Epoch 8/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 139.8542 - val_loss: 206.9584\n",
      "Epoch 9/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 128.7332 - val_loss: 193.3648\n",
      "Epoch 10/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 118.4020 - val_loss: 180.5976\n",
      "Epoch 11/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 108.8552 - val_loss: 168.6540\n",
      "Epoch 12/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 100.0866 - val_loss: 157.5306\n",
      "Epoch 13/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 92.0887 - val_loss: 147.2234\n",
      "Epoch 14/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 84.8523 - val_loss: 137.7275\n",
      "Epoch 15/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 78.3663 - val_loss: 129.0369\n",
      "Epoch 16/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 72.6170 - val_loss: 121.1435\n",
      "Epoch 17/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 67.5870 - val_loss: 114.0375\n",
      "Epoch 18/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 63.2548 - val_loss: 107.7053\n",
      "Epoch 19/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 59.5929 - val_loss: 102.1293\n",
      "Epoch 20/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 56.5669 - val_loss: 97.2853\n",
      "Epoch 21/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 54.1335 - val_loss: 93.1416\n",
      "Epoch 22/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 52.2395 - val_loss: 89.6564\n",
      "Epoch 23/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 50.8219 - val_loss: 86.7765\n",
      "Epoch 24/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 49.8088 - val_loss: 84.4382\n",
      "Epoch 25/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 49.1239 - val_loss: 82.5696\n",
      "Epoch 26/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.6914 - val_loss: 81.0958\n",
      "Epoch 27/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.4426 - val_loss: 79.9439\n",
      "Epoch 28/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.3196 - val_loss: 79.0483\n",
      "Epoch 29/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.2776 - val_loss: 78.3528\n",
      "Epoch 30/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.2839 - val_loss: 77.8119\n",
      "Epoch 31/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.3165 - val_loss: 77.3896\n",
      "Epoch 32/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.3609 - val_loss: 77.0583\n",
      "Epoch 33/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.4084 - val_loss: 76.7968\n",
      "Epoch 34/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.4541 - val_loss: 76.5891\n",
      "Epoch 35/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.4953 - val_loss: 76.4229\n",
      "Epoch 36/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.5310 - val_loss: 76.2891\n",
      "Epoch 37/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.5609 - val_loss: 76.1806\n",
      "Epoch 38/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.5854 - val_loss: 76.0920\n",
      "Epoch 39/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.6050 - val_loss: 76.0190\n",
      "Epoch 40/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.6201 - val_loss: 75.9584\n",
      "Epoch 41/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 48.6316 - val_loss: 75.9077\n",
      "Epoch 42/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 48.6398 - val_loss: 75.8648\n",
      "Epoch 43/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.6454 - val_loss: 75.8282\n",
      "Epoch 44/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.6488 - val_loss: 75.7966\n",
      "Epoch 45/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 48.6503 - val_loss: 75.7690\n",
      "Epoch 46/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.6504 - val_loss: 75.7446\n",
      "Epoch 47/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.6492 - val_loss: 75.7228\n",
      "Epoch 48/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.6470 - val_loss: 75.7032\n",
      "Epoch 49/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.6440 - val_loss: 75.6852\n",
      "Epoch 50/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.6403 - val_loss: 75.6685\n",
      "Epoch 51/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.6361 - val_loss: 75.6529\n",
      "Epoch 52/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.6315 - val_loss: 75.6382\n",
      "Epoch 53/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.6265 - val_loss: 75.6242\n",
      "Epoch 54/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 48.6212 - val_loss: 75.6108\n",
      "Epoch 55/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 48.6157 - val_loss: 75.5979\n",
      "Epoch 56/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.6100 - val_loss: 75.5853\n",
      "Epoch 57/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.6042 - val_loss: 75.5730\n",
      "Epoch 58/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.5982 - val_loss: 75.5610\n",
      "Epoch 59/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.5922 - val_loss: 75.5492\n",
      "Epoch 60/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.5861 - val_loss: 75.5375\n",
      "Epoch 61/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.5799 - val_loss: 75.5260\n",
      "Epoch 62/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.5736 - val_loss: 75.5145\n",
      "Epoch 63/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.5674 - val_loss: 75.5032\n",
      "Epoch 64/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.5610 - val_loss: 75.4919\n",
      "Epoch 65/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.5547 - val_loss: 75.4807\n",
      "Epoch 66/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.5484 - val_loss: 75.4695\n",
      "Epoch 67/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 48.5420 - val_loss: 75.4584\n",
      "Epoch 68/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.5356 - val_loss: 75.4473\n",
      "Epoch 69/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.5292 - val_loss: 75.4362\n",
      "Epoch 70/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.5229 - val_loss: 75.4252\n",
      "Epoch 71/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.5165 - val_loss: 75.4142\n",
      "Epoch 72/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.5101 - val_loss: 75.4032\n",
      "Epoch 73/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.5037 - val_loss: 75.3922\n",
      "Epoch 74/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.4973 - val_loss: 75.3812\n",
      "Epoch 75/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.4909 - val_loss: 75.3703\n",
      "Epoch 76/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.4845 - val_loss: 75.3593\n",
      "Epoch 77/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.4781 - val_loss: 75.3483\n",
      "Epoch 78/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.4717 - val_loss: 75.3374\n",
      "Epoch 79/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.4653 - val_loss: 75.3265\n",
      "Epoch 80/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.4589 - val_loss: 75.3156\n",
      "Epoch 81/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.4525 - val_loss: 75.3046\n",
      "Epoch 82/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.4462 - val_loss: 75.2937\n",
      "Epoch 83/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.4398 - val_loss: 75.2828\n",
      "Epoch 84/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.4334 - val_loss: 75.2719\n",
      "Epoch 85/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.4270 - val_loss: 75.2610\n",
      "Epoch 86/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.4207 - val_loss: 75.2501\n",
      "Epoch 87/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.4143 - val_loss: 75.2393\n",
      "Epoch 88/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.4079 - val_loss: 75.2284\n",
      "Epoch 89/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 48.4016 - val_loss: 75.2175\n",
      "Epoch 90/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.3952 - val_loss: 75.2066\n",
      "Epoch 91/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.3889 - val_loss: 75.1958\n",
      "Epoch 92/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 48.3825 - val_loss: 75.1849\n",
      "Epoch 93/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.3762 - val_loss: 75.1741\n",
      "Epoch 94/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.3698 - val_loss: 75.1632\n",
      "Epoch 95/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.3635 - val_loss: 75.1524\n",
      "Epoch 96/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.3572 - val_loss: 75.1416\n",
      "Epoch 97/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.3508 - val_loss: 75.1307\n",
      "Epoch 98/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.3445 - val_loss: 75.1199\n",
      "Epoch 99/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.3382 - val_loss: 75.1091\n",
      "Epoch 100/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.3318 - val_loss: 75.0983\n",
      "Epoch 101/101\n",
      "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 48.3255 - val_loss: 75.0875\n"
     ]
    }
   ],
   "source": [
    "# training\n",
    "history = model.fit(\n",
    "    X_train_1d,\n",
    "    y_train,\n",
    "    batch_size=16,\n",
    "    epochs=101,\n",
    "    validation_split=0.3,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ffe76528",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-19T20:07:02.651289Z",
     "iopub.status.busy": "2024-10-19T20:07:02.650860Z",
     "iopub.status.idle": "2024-10-19T20:07:02.658886Z",
     "shell.execute_reply": "2024-10-19T20:07:02.657455Z"
    },
    "papermill": {
     "duration": 0.049425,
     "end_time": "2024-10-19T20:07:02.661265",
     "exception": false,
     "start_time": "2024-10-19T20:07:02.611840",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_loss(history):\n",
    "    plt.figure(figsize=(20,5))\n",
    "    plt.plot(history.history['loss'], 'orange', label='Training Loss')\n",
    "    plt.plot(history.history['val_loss'], 'g', label='Vlidation Loss')\n",
    "    plt.xlim([0, 100])\n",
    "    plt.ylim([0, 300])\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "    plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "27307d96",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-19T20:07:02.738591Z",
     "iopub.status.busy": "2024-10-19T20:07:02.738125Z",
     "iopub.status.idle": "2024-10-19T20:07:03.083771Z",
     "shell.execute_reply": "2024-10-19T20:07:03.082482Z"
    },
    "papermill": {
     "duration": 0.387874,
     "end_time": "2024-10-19T20:07:03.086761",
     "exception": false,
     "start_time": "2024-10-19T20:07:02.698887",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8b1d50bd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-19T20:07:03.165030Z",
     "iopub.status.busy": "2024-10-19T20:07:03.164540Z",
     "iopub.status.idle": "2024-10-19T20:07:03.268799Z",
     "shell.execute_reply": "2024-10-19T20:07:03.267325Z"
    },
    "papermill": {
     "duration": 0.146935,
     "end_time": "2024-10-19T20:07:03.271325",
     "exception": false,
     "start_time": "2024-10-19T20:07:03.124390",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
      "Predicted price of a home with 3 rooms: $11K\n",
      "Predicted price of a home with 4 rooms: $14K\n",
      "Predicted price of a home with 5 rooms: $18K\n",
      "Predicted price of a home with 6 rooms: $21K\n",
      "Predicted price of a home with 7 rooms: $24K\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_17/1498741996.py:7: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  print(f'Predicted price of a home with {x[idx]} rooms: ${int(y_pred[idx] * 10 /10)}K')\n"
     ]
    }
   ],
   "source": [
    "# making predictions\n",
    "x = np.array([3, 4, 5, 6, 7])\n",
    "\n",
    "y_pred = model.predict(x)\n",
    "\n",
    "for idx in range(len(x)):\n",
    "    print(f'Predicted price of a home with {x[idx]} rooms: ${int(y_pred[idx] * 10 /10)}K')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "21713989",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-19T20:07:03.350555Z",
     "iopub.status.busy": "2024-10-19T20:07:03.350097Z",
     "iopub.status.idle": "2024-10-19T20:07:03.451999Z",
     "shell.execute_reply": "2024-10-19T20:07:03.450683Z"
    },
    "papermill": {
     "duration": 0.145442,
     "end_time": "2024-10-19T20:07:03.454765",
     "exception": false,
     "start_time": "2024-10-19T20:07:03.309323",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n"
     ]
    }
   ],
   "source": [
    "x = np.linspace(3, 9 ,10)\n",
    "\n",
    "y = model.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0c97d49f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-19T20:07:03.535537Z",
     "iopub.status.busy": "2024-10-19T20:07:03.535099Z",
     "iopub.status.idle": "2024-10-19T20:07:03.543666Z",
     "shell.execute_reply": "2024-10-19T20:07:03.542259Z"
    },
    "papermill": {
     "duration": 0.051516,
     "end_time": "2024-10-19T20:07:03.546388",
     "exception": false,
     "start_time": "2024-10-19T20:07:03.494872",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_data(x_data, y_data, x, y, title=None):\n",
    "    plt.figure(figsize=(15,5))\n",
    "    plt.scatter(x_data, y_data, label='Ground Truth', color='orange', alpha=0.5)\n",
    "    plt.plot(x, y, color='k', label='Model Predictions')\n",
    "    plt.xlim([3, 9])\n",
    "    plt.ylim([0, 60])\n",
    "    plt.xlabel('Average Number of Rooms')\n",
    "    plt.ylabel('Price [$K]')\n",
    "    plt.title(title)\n",
    "    plt.grid(True)\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9d722175",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-19T20:07:03.626615Z",
     "iopub.status.busy": "2024-10-19T20:07:03.626175Z",
     "iopub.status.idle": "2024-10-19T20:07:04.074857Z",
     "shell.execute_reply": "2024-10-19T20:07:04.073519Z"
    },
    "papermill": {
     "duration": 0.491566,
     "end_time": "2024-10-19T20:07:04.077898",
     "exception": false,
     "start_time": "2024-10-19T20:07:03.586332",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data(X_train_1d, y_train, x, y, title='Training Dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4675b940",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-19T20:07:04.158635Z",
     "iopub.status.busy": "2024-10-19T20:07:04.158183Z",
     "iopub.status.idle": "2024-10-19T20:07:04.548956Z",
     "shell.execute_reply": "2024-10-19T20:07:04.547749Z"
    },
    "papermill": {
     "duration": 0.434356,
     "end_time": "2024-10-19T20:07:04.551749",
     "exception": false,
     "start_time": "2024-10-19T20:07:04.117393",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ftEFEREREt3Tu3Dmo1WqUlZUhKChosJdDRERERH8jJtGIiIiIbuP777+HXq+Hl5cXOjs7sWzZMpw/fx4nTpyQvWSeiIiIiP7/49c5iYiIiG6jt7cXK1aswKlTpzBkyBD4+fmhoKCACTQiIiKi/yCeRCMiIiIiIiIiIjKCHxYgIiIiIiIiIiIygkk0IiIiIiIiIiIiI5hEIyIiIiIiIiIiMoJJNCIiIiIiIiIiIiOYRCMiIiIiIiIiIjKCSTQiIiIiIiIiIiIjmEQjIiIiIiIiIiIygkk0IiIiIiIiIiIiI/4Pk2TuMjtvJGEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data(X_test_1d, y_test, x, y, title='Test Dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee932b4c",
   "metadata": {
    "papermill": {
     "duration": 0.041249,
     "end_time": "2024-10-19T20:07:04.633986",
     "exception": false,
     "start_time": "2024-10-19T20:07:04.592737",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "none",
   "dataSources": [],
   "dockerImageVersionId": 30786,
   "isGpuEnabled": false,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.14"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 34.867009,
   "end_time": "2024-10-19T20:07:06.204928",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2024-10-19T20:06:31.337919",
   "version": "2.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}